Paper deep dive
X$^2$Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization
Zichao Zeng, Weijia Fan, Yufan Chen, June Moh Goo, Junwei Zheng, Ruiping Liu, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen, Jan Boehm
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%
Last extracted: 8/23/2026, 2:31:59 AM
Summary
The paper introduces X2Localizer, a framework for Progressive Cross-view Video Geo-localization (PCVG), addressing limitations of existing methods that require full video sequences. X2Localizer employs cross-grained alignment to jointly supervise global prefix-to-aerial retrieval and fine-grained frame-aerial-tile matching, using an asymmetric objective dependent on temporal budget. It also introduces a Sliding-Window Re-Localization (SWRL) strategy for robust incremental localization and failure recovery. Experiments on the GAMa dataset show significant improvements in early localization and robustness under random-start and long-distance scenarios compared to state-of-the-art methods.
Entities (7)
Relation Signals (6)
X2Localizer → solves → PCVG
confidence 95% · To explore PCVG, we introduce X2Localizer, a cross-grained alignment framework...
PCVG → extends → CVG
confidence 94% · we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG
X2Localizer → uses → SWRL
confidence 93% · Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy... With SWRL, our approach further enables robust progressive localization
X2Localizer → evaluatedon → GAMa
confidence 92% · Extensive experiments show that X2Localizer preserves conventional full-video performance... we reconstruct the protocol of the GAMa dataset
X2Localizer → contains → GeoAdapter
confidence 90% · we insert a lightweight GeoAdapter module into each Transformer block
X2Localizer → outperforms → GAReT
confidence 88% · In the challenging single-frame setting, X2Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method.
Cypher Suggestions (0)
No Cypher suggestions yet.
Abstract
Abstract:Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations. In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X$^2$Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame--aerial-tile matching with a budget-dependent asymmetric objective. Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X$^2$Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X$^2$Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.
Tags
Links
- Source: https://arxiv.org/abs/2608.16658v1
- Canonical: https://arxiv.org/abs/2608.16658v1
Trouble viewing inline? Open PDF directly →
Full Text
61,496 characters extracted from source content.
Expand or collapse full text
X2Localizer X2Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization Zichao Zeng Weijia Fan Yufan Chen June Moh Goo Junwei Zheng Ruiping Liu Kunyu Peng Jiaming Zhang Rainer Stiefelhagen Jan Boehm Abstract Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations. In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X2Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching with a budget-dependent asymmetric objective. Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X2Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X2Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment. The code is publicly available at https://zichaozeng.github.io/X2Localizer. †email: zichao.zeng.21@ucl.ac.uk†email: weijia.fan@ualberta.ca†email: yufan.chen@kit.edu†email: june.goo.21@ucl.ac.uk†email: junwei.zheng@kit.edu†email: ruiping.liu@kit.edu†email: kunyu.peng@kit.edu†email: jiamingzhang@hnu.edu.cn†email: rainer.stiefelhagen@kit.edu†email: j.boehm@ucl.ac.uk†affiliation: University College London, London, UK †affiliation: Karlsruhe Institute of Technology, Karlsruhe, Germany †affiliation: Hunan University, Changsha, China †affiliation: University of Alberta, Edmonton, Canada †affiliation: Shenzhen University, Shenzhen, China 1 Introduction Visual geo-localization aims to estimate the geographic location of a query image by matching it against geo-tagged reference imagery [41, 40, 38, 26, 48, 7, 5, 27, 33]. Cross-view geo-localization, which aligns ground-view observations with aerial or satellite imagery, has attracted significant attention due to its applications in autonomous navigation, robotics, digital twins, and urban computing [10, 45, 39, 9, 1, 3]. While early studies focus on single-image matching, recent works extend the problem to Cross-view Video Geo-localization (CVG), where a ground-view video is matched against a large aerial image [29, 18, 20, 44, 25]. By aggregating temporal cues across frames, these methods consistently outperform single-frame approaches, demonstrating the effectiveness of spatio-temporal modeling. Despite these advances, early methods in cross-view geo-localization either aggregate sequence features to predict a single coarse location for the entire video [44], or rely on explicit geometric projection based on camera parameters and estimated relative poses to align ground and aerial views [25]. More recent approaches achieve frame-level localization using purely visual representations [18, 29]. However, they are typically designed for offline inference, assuming access to the complete query sequence before trajectory estimation, and do not explicitly consider streaming or incremental localization settings. Such an evaluation protocol does not align with real-world deployment scenarios. In practical systems, video streams arrive progressively, and localization must be performed incrementally [16, 15, 11]. The model should produce reliable predictions from short prefixes (e.g, a single frame or a few seconds), adapt to arbitrary starting timestamps, and remain robust to interruptions or partial observations. Empirically, we observe that when evaluated under such progressive conditions, existing CVG methods exhibit noticeable performance degradation, revealing a fundamental gap between benchmark assumptions and practical requirements. To bridge this gap, we reformulate the task as Progressive Cross-view Video Geo-localization (PCVG). Instead of assuming access to a complete fixed-length sequence, we require the model to localize under varying temporal budgets, ranging from single-frame to full-length videos. To enable systematic evaluation, we reconstruct the protocol of the GAMa dataset [29] and establish a new progressive benchmark that supports multi-duration prefix evaluation, random-start testing, and long-distance or interruption scenarios. This reformulation not only provides a more realistic evaluation setting, but also exposes intrinsic limitations of existing coarse-grained global matching strategies. Figure 1: Compared to traditional cross-view video geo-localization (CVG), our proposed Progressive CVG (PCVG) enables precise localization (1) at arbitrary timestamps, (2) for varying video lengths, and (3) remains robust to interruptions or missing frames. A central challenge in PCVG lies in the limited contextual information available in short video segments. Conventional approaches primarily rely on global-to-global matching between a full aerial image and a temporally aggregated ground-video representation. Such a design is inherently fragile when only partial observations are available [16]. To address this issue, we propose X2Localizer, a cross-grained and cross-view alignment framework that extends existing CVG methods [18, 48] to the PCVG setting. Inspired by multi-grained contrastive learning [19, 37, 14, 35], our key insight is to establish multi-grained correspondences between global and local aerial representations and ground-level temporal observations under varying temporal budgets. X2Localizer therefore jointly models global image-to-video alignment and fine-grained patch-to-frame alignment, with an asymmetric objective that places greater emphasis on local frame-level cues for shorter prefixes and stronger global alignment for longer observations. By modeling these cross-grained interactions within a unified contrastive learning framework, X2Localizer enables reliable cross-view coarse localization under varying temporal budgets, naturally facilitating subsequent incremental frame-to-frame refinement and re-localization. Furthermore, inspired by ground-level sequence-based localization methods [8, 30], we introduce a Sliding-Window Re-Localization (SWRL) strategy that preserves stable alignment signals even in long-range or interrupted video streams. Our contributions are summarized as follows: • We redefine cross-view video geo-localization as Progressive Cross-view Video Geo-localization (PCVG), a deployment-oriented setting supporting multi-duration, random-start, and long-distance evaluation. We reconstruct the GAMa dataset protocol to establish a new progressive benchmark. • We propose X2Localizer, a cross-grained alignment framework that combines global prefix-to-aerial alignment with token-aggregated frame–aerial-tile alignment. It asymmetrically weights these objectives according to the available temporal context, enabling robust localization under partial observations. • We introduce a Sliding-Window Re-Localization (SWRL) inference strategy that allows dynamic re-localization over long video streams, enabling failure recovery and sustained long-range deployment. • Extensive experiments demonstrate that X2Localizer achieves competitive performance under the conventional full-video protocol, while substantially improving prefix and progressive localization performance under the proposed PCVG benchmark. 2 Related Work Cross-view Image Geo-localization. Cross-view image geo-localization aims to match ground-view images to geo-tagged aerial or satellite imagery [48, 7, 5, 46]. Early methods primarily rely on Siamese or triplet-based metric learning frameworks to learn view-invariant representations. Representative works include SAFA [22], DSM [23, 24], and L2LTR [34], which introduce orientation alignment, polar transformation, or dynamic similarity matching to mitigate severe viewpoint discrepancies between ground and aerial views. Subsequent approaches explore attention mechanisms and transformer-based architectures to model global context more effectively. For example, TransGeo [48] demonstrates that pure transformer models can achieve strong cross-view alignment without explicit geometric transformations. More recent works further investigate fine-grained correspondence learning and pose-aware modeling to improve localization precision [31, 21, 32, 12, 6] or leverage the ability of large language models [38]. However, these methods operate on single images and do not model temporal continuity, making them insufficient for video-based progressive localization. Cross-view Video Geo-localization. To mitigate the limited field-of-view of single ground images, SeqGeo [44] aggregates short ground-view sequences for cross-view matching, demonstrating improved robustness over single-frame methods. Extending this direction, GAMa [29] introduces the first large-scale cross-view video dataset with a hierarchical coarse-to-fine strategy, while CVLNet [25] incorporates geometric projection and temporal constraints but relies on camera intrinsics and odometry. More recently, GAReT [18] adapts image geo-localization models to video via lightweight adapters and autoregressive retrieval, achieving state-of-the-art performance under fixed-length settings. Despite their strong fixed-length performance, existing CVG methods generally assume that the complete query video is available before inference. In contrast, ground-level sequence-based localization has highlighted the importance of progressive inference, incremental updates, and re-localization for real-world robustness [16, 15, 8, 30, 11, 47]. PCVG transfers these operational requirements to cross-view video geo-localization, where frames arrive incrementally and the system must handle arbitrary starts, interruptions, and cross-region transitions. 3 Methodology 3.1 Problem Formulation The proposed task of PCVG is shown in Fig. 1. Given a ground-view video sequence and a geo-tagged aerial image database, the goal is to localize the video frames by cross-view retrieval under varying temporal budgets. Let =Vii=1NV=\V_i\_i=1^N denote a set of ground-view videos. Each video V=tt=1TV=\ f_t\_t=1^T consists of T ordered frames. For each frame t f_t, there exists a corresponding geo-tagged aerial tile (small GPS-centered image) t∈tile a_t _tile. In addition, each video V is associated with a high-resolution aerial image Aglobal∈globalA^global _global that covers the entire geographic region of the trajectory. Let globalA_global and tileA_tile denote the global aerial gallery and tile-level aerial gallery, respectively. Existing CVG methods [29, 18] assume access to the complete video V before localization. They typically perform: (1) coarse retrieval by matching the full video representation to globalA_global, and (2) backtracking fine-grained frame-tile retrieval within the selected region by tileA_tile. Formally, a ground-view encoder ϕv _v and an aerial tile encoder ϕa _a are pretrained by frame-tile matching, i.e., ϕv(t)≈ϕa(t) _v( f_t)≈ _a( a_t). Subsequently, with lightweight adapters, a video encoder Φv _v and a global aerial image encoder Φa _a are learned such that Φv(V(T))≈Φa(Aglobal) _v(V^(T))≈ _a(A^global). However, during inference, Φv _v and Φa _a are first employed for coarse retrieval, followed by ϕv _v and ϕa _a backtracking to retrieve each frame individually. This formulation implicitly assumes a fully observed and uninterrupted video sequence. In contrast, we consider a progressive formulation where a prefix of the video is observable. Let V(τ)=tt=1τV^(τ)=\ f_t\_t=1^τ denote the first τ frames of V, where τ∈1,…,Tτ∈\1,…,T\. The model is required to perform localization under varying temporal budgets τ, including single-frame (τ=1τ=1), short-clip, half-length, and full-length (τ=Tτ=T) scenarios. Our objective is to learn representations that remain discriminative for every prefix length τ. Specifically, the cosine similarity cos(Φv(V(τ)),Φa(Aglobal)) \! ( _v(V^(τ)), _a(A^global) ) should be maximized for the correct aerial candidate and suppressed for mismatched candidates. Simultaneously, fine-grained frame–tile alignment is preserved through cos(ϕv(t),ϕa(t)) ( _v( f_t), _a( a_t)). This progressive formulation introduces two key challenges: (i) limited contextual information when τ is small, and (i) robustness under interruption, restart, or cross-region transitions in long videos. We refer to this deployment-oriented formulation as PCVG. (a) Adaptation (b) Cross-grained Alignment Objective Figure 2: (a) GeoAdapter adapts frozen pretrained dual-tower encoders from image-tile matching to video-aerial matching. (b) For each temporal budget, our objective combines global video-to-aerial alignment, frame-tile alignment, and ranking distillation; shorter prefixes receive stronger supervision, while longer prefixes emphasize global alignment. 3.2 Pretraining and Adaptation for Cross-view Representation Following previous works [48, 18, 22, 43, 42, 49], we adopt a dual-tower architecture with a ground-view encoder ϕv _v and an aerial-tile encoder ϕa _a. Similar to [48, 18], both encoders use the same distilled ViT backbone [28] but do not share weights. Given an image input X, the image representation is obtained by averaging the projected classification and distillation tokens, i.e., ϕ⋆()=L2Norm((cls(⋆)+dist(⋆))/2),⋆∈v,a. _ ( X)=L2Norm ((h_cls^( )+h_dist^( ))/2 ), ∈\v,a\. (1) We pretrain the dual-tower encoders using frame-to-aerial-tile pairs (t,t)( f_t, a_t) with a single-direction soft-margin contrastive loss ℒsmclL_smcl from the ground side to the aerial side. The full pretraining objective is provided in the supplementary material. To extend the pretrained image encoders ϕv _v and ϕa _a to video-to-global matching, we follow [18, 17, 36] and insert a lightweight GeoAdapter module into each Transformer block, while freezing the pretrained spatial backbone (see Fig. 2(a)). Similar to GAReT [18], we first optimize the GeoAdapter using complete videos and their corresponding global aerial images. Given a mini-batch of B matched video-aerial pairs (Vi(T),Aiglobal)i=1B\(V_i^(T),A_i^global)\_i=1^B, we stack the full-video embeddings and global aerial embeddings as (g,T)=[1(g,T),…,B(g,T)]⊤∈ℝB×d,(g)=[1(g),…,B(g)]⊤∈ℝB×d,V^(g,T)=[v_1^(g,T),…,v_B^(g,T)] ^B× d, ^(g)=[a_1^(g),…,a_B^(g)] ^B× d, (2) where i(g,T)=Φv(Vi(T))v_i^(g,T)= _v(V_i^(T)), i(g)=Φa(Aiglobal)a_i^(g)= _a(A_i^global), and d denotes the aligned feature dimension. All embeddings are ℓ2 _2-normalized, so their dot products correspond to cosine similarity. The batch-wise global similarity is computed as g(T)=(g,T)(g)⊤∈ℝB×B, s_g^(T)=V^(g,T)A^(g) ^B× B, (3) where g(T)[i,j] s_g^(T)[i,j] denotes the similarity between the i-th ground-view video and the j-th global aerial image. We use a row-wise cross-entropy retrieval loss from the ground/video side to the aerial side: ℒce()=−1B∑i=1Blogexp([i,i]/τc)∑j=1Bexp([i,j]/τc),L_ce( s)=- 1B _i=1^B ( s[i,i]/ _c) _j=1^B ( s[i,j]/ _c), (4) where τc _c is the temperature parameter. The full-video adaptation objective is therefore ℒfull=ℒce(g(T))L_full=L_ce( s_g^(T)). This objective adapts the cross-view representation to full-video global localization before introducing cross-grained alignment for different temporal budgets. 3.3 Asymmetric Cross-grained Alignment Objective In the early stage, we train the model only with the full-video adaptation objective in Sec. 3.2, which provides stable global alignment between complete ground-view videos and global aerial images. However, in PCVG, the model must align ground-view observations of different temporal lengths τ∈⊆1,…,Tτ \1,…,T\ with the same global aerial gallery. Unlike previous CVG methods that mainly rely on full-video global supervision, we introduce an asymmetric cross-grained alignment objective to jointly supervise global video-aerial matching and fine-grained token-level (frame-tile) matching under different temporal budgets (Fig. 2(b)). Given a temporal budget τ, the prefix of the i-th video is denoted as Vi(τ)=i,tt=1τV_i^(τ)=\ f_i,t\_t=1^τ. The adapted encoders produce the prefix-level video embedding i(g,τ)=Φv(Vi(τ))∈ℝdv_i^(g,τ)= _v(V_i^(τ)) ^d and the global aerial embedding j(g)=Φa(Ajglobal)∈ℝda_j^(g)= _a(A_j^global) ^d. For a mini-batch of B matched video-aerial pairs, we stack the embeddings as (g,τ)V^(g,τ) and (g)A^(g). The global prefix-to-aerial similarity is computed as g(τ)=(g,τ)(g)⊤∈ℝB×B s_g^(τ)=V^(g,τ)A^(g) ^B× B, where g(τ)[i,j] s_g^(τ)[i,j] measures the global similarity between the i-th video prefix and the j-th aerial image. The global alignment loss is then defined as ℒg(τ)=ℒce(g(τ))L_g^(τ)=L_ce( s_g^(τ)), where ℒceL_ce is the row-wise retrieval cross-entropy in Eq. 4. To provide fine-grained cross-view supervision, we further compute local token-level frame-tile similarities. Let i(τ)=[i,1,…,i,K]⊤∈ℝK×dP_i^(τ)=[p_i,1,…,p_i,K] ^K× d denote the K ground/video tokens of the i-th prefix Vi(τ)V_i^(τ), and let j=[j,1,…,j,L]⊤∈ℝL×dQ_j=[q_j,1,…,q_j,L] ^L× d denote the L aerial tile tokens of the j-th global aerial image AjglobalA_j^global. For each pair (i,j)(i,j), the token-token similarity is tok(τ)[i,j,k,l]=i,k⊤j,l,tok(τ)∈ℝB×B×K×L. s_tok^(τ)[i,j,k,l]=p_i,k q_j,l, s_tok^(τ) ^B× B× K× L. (5) where K=τK=τ in Vi(τ)V_i^(τ) and L is the number of aerial tiles from AjglobalA_j^global. Thus, each video-aerial pair has a K×LK× L frame-tile similarity map, while the whole mini-batch forms a four-dimensional similarity tensor. We aggregate the token-level similarities with a two-stage soft aggregation. For each ground/video token, we first softly aggregate over aerial tile tokens: ri,j,k(τ)=∑l=1Lαi,j,k,l(τ)tok(τ)[i,j,k,l],αi,j,k,l(τ)=exp(tok(τ)[i,j,k,l]/τf)∑l′=1Lexp(tok(τ)[i,j,k,l′]/τf).r_i,j,k^(τ)= _l=1^L _i,j,k,l^(τ) s_tok^(τ)[i,j,k,l], _i,j,k,l^(τ)= ( s_tok^(τ)[i,j,k,l]/ _f) _l =1^L ( s_tok^(τ)[i,j,k,l ]/ _f). (6) Here, τf _f is the token soft-aggregation temperature. We then softly aggregate the resulting ground-token scores: grd(τ)[i,j]=∑k=1Kρi,j,k(τ)ri,j,k(τ),ρi,j,k(τ)=exp(ri,j,k(τ)/τf)∑k′=1Kexp(ri,j,k′(τ)/τf). s_grd^(τ)[i,j]= _k=1^K _i,j,k^(τ)r_i,j,k^(τ), _i,j,k^(τ)= (r_i,j,k^(τ)/ _f) _k =1^K (r_i,j,k ^(τ)/ _f). (7) Symmetrically, for each aerial tile token, we softly aggregate over ground/video tokens: ui,j,l(τ)=∑k=1Kβi,j,k,l(τ)tok(τ)[i,j,k,l],βi,j,k,l(τ)=exp(tok(τ)[i,j,k,l]/τf)∑k′=1Kexp(tok(τ)[i,j,k′,l]/τf),u_i,j,l^(τ)= _k=1^K _i,j,k,l^(τ) s_tok^(τ)[i,j,k,l], _i,j,k,l^(τ)= ( s_tok^(τ)[i,j,k,l]/ _f) _k =1^K ( s_tok^(τ)[i,j,k ,l]/ _f), (8) followed by a soft aggregation over aerial tokens: aer(τ)[i,j]=∑l=1Lωi,j,l(τ)ui,j,l(τ),ωi,j,l(τ)=exp(ui,j,l(τ)/τf)∑l′=1Lexp(ui,j,l′(τ)/τf). s_aer^(τ)[i,j]= _l=1^L _i,j,l^(τ)u_i,j,l^(τ), _i,j,l^(τ)= (u_i,j,l^(τ)/ _f) _l =1^L (u_i,j,l ^(τ)/ _f). (9) The final fine-grained similarity is f(τ)[i,j]=12(grd(τ)[i,j]+aer(τ)[i,j]),f(τ)∈ℝB×B. s_f^(τ)[i,j]= 12 ( s_grd^(τ)[i,j]+ s_aer^(τ)[i,j] ), s_f^(τ) ^B× B. (10) Although f(τ) s_f^(τ) is obtained by aggregating token similarities from two complementary directions, the retrieval loss is applied only in the ground-to-aerial direction: ℒf(τ)=ℒce(f(τ))L_f^(τ)=L_ce( s_f^(τ)). The asymmetric cross-grained alignment objective for temporal budget τ is ℒalign(τ)=λg(τ)ℒg(τ)+λf(τ)ℒf(τ).L_align^(τ)= _g^(τ)L_g^(τ)+ _f^(τ)L_f^(τ). (11) The weights λg(τ) _g^(τ) and λf(τ) _f^(τ) balance global and fine-grained supervision for different temporal lengths. Shorter prefixes rely more on fine-grained local evidence, while longer prefixes and full videos place more emphasis on global video-aerial alignment. In addition to the supervised alignment objective, we preserve the ranking structure learned from full-video adaptation through row-wise ranking distillation. Given a student similarity matrix s and a teacher similarity matrix ~ s, we define rank(,~)=τd2KL(softmax(~/τd)∥softmax(/τd)),D_rank( s, s)= _d^2KL (softmax( s/ _d)\; \|\;softmax( s/ _d) ), (12) where τd _d is the distillation temperature, and the softmax and KL divergence are computed row-wise over the aerial gallery. For prefix-to-full self-distillation, shorter prefixes are encouraged to match the full-prefix ranking distribution produced by the current student: ℒself=1|∖T|∑τ∈,τ<Trank(g(τ),g(T)).L_self= 1|T \T\| _τ ,τ<TD_rank ( s_g^(τ), s_g^(T) ). (13) We further use the frozen full-video model from the early training stage as a teacher for the full-prefix student: ℒteacher=rank(g(T),~g(T)),L_teacher=D_rank ( s_g^(T), s_g^(T) ), (14) where ~g(T) s_g^(T) denotes the global similarity matrix produced by the early stage full-video teacher. The final training objective is ℒcross=∑τ∈γτℒalign(τ),ℒtotal=ℒcross+ηselfℒself+ηteacherℒteacher.L_cross= _τ _τL_align^(τ), _total=L_cross+ _selfL_self+ _teacherL_teacher. (15) Here, γτ _τ weights temporal budget τ, while ηself _ self and ηteacher _ teacher weight the two distillation terms. (a) SWRL Strategy (b) Challenging Cases Figure 3: (a) Sliding-window Re-localization (SWRL) refreshes the candidate region set candA_cand every Δ frames to support subsequent incremental refinement. (b) It improves robustness in long-range localization and enables failure recovery. 3.4 Progressive Inference Strategy During inference, we localize a query video with either a prefix or the full sequence. Given V(τ)=tt=1τV^(τ)=\ f_t\_t=1^τ, where τ∈1,…,Tτ∈\1,…,T\, we compute (g,τ)=Φv(V(τ))v^(g,τ)= _v(V^(τ)). For each global aerial candidate Ajglobal∈globalA_j^global _global, we use the same mixed-resolution matching strategy as in training: smix(τ)(j)=12(sg(τ)(j)+sf(τ)(j)),sg(τ)(j)=(g,τ)⊤j(g).s_mix^(τ)(j)= 12 (s_g^(τ)(j)+s_f^(τ)(j) ), s_g^(τ)(j)=v^(g,τ) a_j^(g). (16) Here, sf(τ)(j)s_f^(τ)(j) is obtained by applying the same two-direction token aggregation to the frame-token and aerial-tile-token similarities. We select the top-KcK_c global aerial regions according to smix(τ)(j)s_mix^(τ)(j) to form cand(τ)A_cand^(τ). When τ=Tτ=T, this becomes full-video retrieval; when τ<Tτ<T, it enables early localization with partial observations. For long video streams that may traverse multiple geographic regions, an initially retrieved candidate set may gradually become suboptimal. Inspired by [30, 8], we introduce the Sliding-window Re-localization (SWRL) strategy to periodically refresh the candidate set (Fig. 3(a)). Given a continuous stream tt=1∞\ f_t\_t=1^∞, we define a sliding window W(t)W^(t). Within this window, we construct a window-prefix W(t,τ)W^(t,τ), which is fed into the video encoder. W(t)=t−Δ+1,…,t,W(t,τ)=t−Δ+1,…,t−Δ+τ,τ∈1,…,Δ,W^(t)=\ f_t- +1,…, f_t\, W^(t,τ)=\ f_t- +1,…, f_t- +τ\, τ∈\1,…, \, (17) where Δ denotes the window size used for re-localization. The window-prefix embedding and its mixed-resolution retrieval score are computed as W(g,τ)=Φv(W(t,τ)),sW(τ)(j)=smix(τ)(W(t,τ),Ajglobal),v_W^(g,τ)= _v(W^(t,τ)), s_W^(τ)(j)=s_mix^(τ)(W^(t,τ),A_j^global), (18) where smix(τ)s_mix^(τ) is defined in Eq. 16. The first coarse retrieval requires accumulating a prefix, which introduces a warm-start cost before localization is initialized. After initialization, frame-level geo-localization is performed using the candidate tile gallery. SWRL periodically refreshes the candidate set using the prefix of the current sliding window W(t,τ)W^(t,τ), and the geo-tagged tile reference gallery is updated. This mechanism enables (1) cross-region or long-distance localization when the trajectory moves beyond the initially retrieved geographic area, and (2) failure recovery under occlusion, visual ambiguity, or temporary interruption, by reinitializing localization using only a short temporal budget within the latest window (Fig. 3). SWRL requires no additional training or supervision and naturally integrates with progressive inference. 4 Experiments and Results 4.1 Implementation Details We adopt DeiT-Small [28], pretrained on ImageNet [4], as the backbone for ground-view and aerial branches in the dual-tower architecture. The image-level encoders ϕv _v and ϕa _a are pretrained with frame–aerial-tile pairs using the ground-to-aerial soft-margin contrastive objective. We then freeze the pretrained spatial backbone and insert GeoAdapter modules into the Transformer blocks to obtain the video/global-aerial encoders Φv _v and Φa _a. The early adaptation stage uses only the full-video global retrieval loss ℒfullL_full to train the adapters, and is run for at most 50 epochs with early stopping patience 10; the resulting full-video model is used as the teacher for progressive training. In the progressive stage, we train with temporal budgets τ∈=1,2,4,8τ =\1,2,4,8\, with K=τK=τ, corresponding to the first frame, 5s, 20s, and the full 40s video. For τ=1,2,4,8τ=1,2,4,8, respectively, the budget weights γτ _τ are set to (0.05,0.10,0.25,2.00)(0.05,0.10,0.25,2.00). We set the asymmetric component weights (λg(τ),λf(τ))( _g^(τ), _f^(τ)) to (1,2)(1,2), (1,1)(1,1), (1,0.5)(1,0.5), and (1,0)(1,0), so short prefixes receive stronger fine-grained supervision while the full video is supervised only by global alignment. Prefix-to-full self-distillation and early-teacher ranking distillation are weighted by ηself=0.2 _ self=0.2 and ηteacher=1.0 _ teacher=1.0, respectively. We set the contrastive and distillation temperatures τc=τd=0.07 _c= _d=0.07 and the token soft-aggregation temperature τf=0.01 _f=0.01. Both adaptation stages use Adam with learning rate 1×10−41× 10^-4, batch size B=8B=8, mixed precision, and are trained for at most 50 epochs with patience 10 on a single NVIDIA RTX PRO 6000 GPU. For SWRL, we refresh candidate regions every 20 seconds (Δ ). Dataset. Following prior work, we use the train-day split of the GAMa dataset for training and the val-day split for evaluation. GAMa is a cross-view geo-localization benchmark designed for frame-to-frame matching. Each sample contains a ∼ 40s street-view video (from BDD100K) paired with (1) a global aerial image covering the surrounding region, and (2) frame-level geo-tagged aerial image tiles corresponding to each video frame. The train-day split contains 21,144 video-global aerial pairs and approximately 790K frame-level aerial matches. The val-day split includes 3,103 videos and around 116K frame-level matches. To evaluate long-range localization ability, we further construct a val-long-distance subset by concatenating two sequences from the same geographic source whose temporal gap is no more than two minutes. This subset contains 127 long sequences and approximately 6K frame-level pairs for evaluation. Evaluation Protocols. We evaluate X2Localizer under both the conventional CVG protocol and the proposed PCVG protocol. The conventional protocol assumes that the full 40-second video is available before coarse retrieval and retrospective frame-level localization, whereas PCVG evaluates localization under partial, restarted, and long-range streaming observations. We consider four settings. First, for global coarse retrieval, we retrieve the corresponding global aerial image using =1,2,4,8T=\1,2,4,8\, covering both shorter prefixes and the full video. Second, for coarse-to-fine frame-level localization, the prefix-level coarse retriever selects the top-10 global aerial candidate regions, from which we construct the tile gallery and rerank frame-level aerial tiles using the image-level retriever. A frame prediction is correct if its GPS location is within 80 meters of the ground truth. Third, for random-start recovery, we sample one valid starting timestamp for each validation video, perform prefix-based coarse re-localization from that timestamp, and incrementally retrieve frame-level matches on the remaining frames. This setting simulates localization restart after interruption, tracking failure, or missing context. Fourth, for long-distance progressive localization, we evaluate continuous frame-level retrieval on the long-distance subset, where SWRL periodically refreshes candidate regions using the prefix of the current sliding window rather than relying on a single initial coarse retrieval. We report Recall@1, Recall@5, Recall@10, and Recall@1%. 4.2 Coarse Retrieval (V-to-globalA_global) We compare X2Localizer with CVLNet [25], GAMa [29], GAReT [18], and general-purpose video backbones including VideoSWIN [13] and TimeSformer [2]. GAReT shares the same pretrained dual-tower cross-view encoder as our method but optimizes video-to-global matching mainly under the full-video setting. We also include two DeiT-based baselines to isolate the effect of the proposed objective. DeiT denotes a fine-tuned DeiT dual-tower model without GeoAdapter adaptation, while DeiT⋆ uses the same backbone but is trained with our asymmetric cross-grained alignment objective. Detailed baseline descriptions are provided in the supplementary material. Table 1 reports coarse retrieval under different temporal budgets. Under the conventional full-video protocol, X2Localizer achieves performance comparable to GAReT, indicating that progressive training does not sacrifice standard CVG performance. The advantage becomes more evident under the PCVG protocol. When the input is shortened to 20 seconds, 5 seconds, or a single frame, X2Localizer consistently improves over GAReT, with the largest gain appearing in the most constrained single-frame setting. This confirms that relying only on full-video global alignment is insufficient for progressive localization. By contrast, our asymmetric cross-grained objective directly supervises both prefix-level global retrieval and token-aggregated frame-tile evidence, allowing the representation to remain discriminative even when temporal context is limited. In addition, comparing DeiT⋆ with DeiT shows that the proposed asymmetric cross-grained objective consistently improves retrieval even without GeoAdapter adaptation. For τ=1,2,4,8τ=\1,2,4,8\, it improves Recall@1 by +4.9, +6.1, +7.1, and +6.3 for DeiT, respectively. This indicates that asymmetric cross-grained supervision is broadly beneficial for progressive retrieval, not only for the final X2Localizer architecture. Model Backbone #params Latency R@1 R@5 R@10 R@1% Full 40s video-to-global aerial image τ=8τ=8 TimeSFormer [2] ViT-B 243M 14.2 20.1 44.5 55.6 83.5 VideoSWIN [13] Swin-B 175M 14.3 20.4 45.9 59.9 88.0 CVLNet [25] VGG16 17M 23.9 0.4 1.3 2.7 15.4 GAMa [29] Mixed 23M 32.7 12.2 - 35.3 49.3 DeiT [28] DeiT-S 45M 2.9 20.5 49.9 63.6 98.7 DeiT⋆ [28] DeiT-S 45M 2.9 26.8 61.6 74.2 99.5 GAReT [18] DeiT-S 47M 6.9 50.2 83.3 90.7 99.8 X2Localizer (ours) DeiT-S 47M 6.9 50.3 83.9 91.0 99.8 Improv. +0.1 +0.6 +0.3 0 First 20s clip-to-global aerial image τ=4τ=4 DeiT [28] DeiT-S 45M 2.1 16.2 43.0 57.7 97.7 DeiT⋆ [28] DeiT-S 45M 2.1 23.3 54.1 67.4 98.6 GAReT [18] DeiT-S 47M 5.2 41.4 75.0 84.4 99.2 X2Localizer (ours) DeiT-S 47M 5.2 42.5 78.2 86.7 99.7 Improv. +1.1 +3.2 +2.3 +0.5 First 5s clip-to-global aerial image τ=2τ=2 DeiT [28] DeiT-S 45M 1.2 10.3 31.9 44.4 93.7 DeiT⋆ [28] DeiT-S 45M 1.2 16.4 42.7 56.2 96.8 GAReT [18] DeiT-S 47M 4.7 25.9 55.0 66.8 96.4 X2Localizer (ours) DeiT-S 47M 4.7 29.1 61.9 72.8 98.6 Improv. +3.2 +6.9 +6.0 +2.2 First frame-to-global aerial image τ=1τ=1 DeiT [28] DeiT-S 45M 1.0 7.4 24.6 35.8 90.3 DeiT⋆ [28] DeiT-S 45M 1.0 12.3 35.6 48.6 94.2 GAReT [18] DeiT-S 47M 4.4 16.9 40.4 52.2 92.6 X2Localizer (ours) DeiT-S 47M 4.4 21.6 50.9 63.7 97.2 Improv. +4.7 +10.5 +11.5 +4.6 Table 1: Coarse retrieval performance under varying prefix budgets τ. Matching is evaluated between V(τ)V^(τ) and globalA_global, reported in Recall@k (%) and inference latency (ms/video). The best results are highlighted in bold. 4.3 Fine-grained Retrieval (V-to-tileA_tile) We next evaluate whether better prefix-level coarse retrieval leads to stronger frame-level localization. Following prior work, a frame-level prediction is considered correct if the predicted GPS location falls within 0.05 miles (nearly 80 m) of the ground-truth location. We compare our method with 23, L2LTR 34, GAMa 29 including its hierarchical variant GAMa⋆, and GAReT. For GAReT and X2Localizer, the fine-grained tile gallery is constructed from the top-10 global aerial candidates retrieved by the corresponding coarse model. Table 2 reports frame-level localization under different coarse-retrieval budgets. In the full-video setting, X2Localizer achieves performance comparable to GAReT, showing that the proposed progressive objective preserves the standard retrospective localization ability. Under shorter prefix budgets, however, X2Localizer consistently improves fine-grained retrieval. The improvement is especially clear for the 5-second and single-frame settings, where the initial coarse gallery is more difficult to construct reliably. This demonstrates that the asymmetric cross-grained objective improves not only global aerial retrieval but also the quality of the downstream tile gallery. We further evaluate SWRL in the same setting. Instead of relying on a single initial coarse retrieval, SWRL periodically refreshes the candidate aerial region using the latest sliding-window prefix. This dynamic update mitigates error accumulation and allows the fine-grained retriever to recover from suboptimal initial candidates. The gains are most pronounced under short warm-start budgets, confirming that progressive coarse re-localization is beneficial for practical online deployment. However, broad-rank metrics may remain comparable or slightly decrease because the refreshed gallery focuses on the current local region. Model Backbone #params Latency R@1 R@5 R@10 R@1% Full 40s video-to-global aerial image τ=8τ=8 Shi et al 23 VGG16 18M 2.0 9.6 18.1 26.6 71.9 L2LTR 34 ViT-B 196M 12.7 11.7 20.8 28.2 87.1 GAMa 29 Mixed 49M 4.3 15.2 27.2 33.8 91.9 GAMa⋆ 29 Mixed 72M 41.3 18.3 27.6 32.7 - GAReT 18 DeiT-S 45M 3.6 46.8 71.8 81.1 91.4 X2Localizer (ours) DeiT-S 45M 3.6 46.5 72.0 81.4 91.8 First 20s clip-to-global aerial image τ=4τ=4 GAReT 18 DeiT-S 45M 3.6 45.9 68.9 77.0 85.8 + SWRL 4.9(/20s) 45.9 67.0 74.0 78.6 X2Localizer (ours) DeiT-S 45M 3.6 46.3 70.3 78.8 87.7 + SWRL 4.9(/20s) 47.4 70.0 77.3 82.1 First 5s clip-to-global aerial image τ=2τ=2 GAReT 18 DeiT-S 45M 3.6 39.2 57.6 63.8 70.2 + SWRL 4.9(/20s) 44.8 62.5 66.4 67.8 X2Localizer (ours) DeiT-S 45M 3.6 41.7 61.5 68.0 74.7 + SWRL 4.9(/20s) 48.0 67.3 71.6 72.9 First frame-to-global aerial image τ=1τ=1 GAReT 18 DeiT-S 45M 3.6 32.1 47.1 51.7 56.6 + SWRL 4.9(/20s) 39.2 54.4 56.0 56.0 X2Localizer (ours) DeiT-S 45M 3.6 37.5 55.2 60.5 66.3 + SWRL 4.9(/20s) 44.9 62.2 64.5 64.5 Table 2: Frame-level localization performance with different prefix budgets. Retrieval is performed for all frames after coarse retrieval, reported in Recall@k (%) and inference latency (ms/frame). Bold denotes the best results under the same inference strategy, and results in red indicate performance improvements brought by SWRL. 4.4 Random-start Recovery To evaluate recovery ability after interruption or localization failure, we conduct a random-start recovery experiment. For each video, we randomly sample a temporal position and initialize localization using only a short prefix starting from that position. We evaluate both prefix-based coarse retrieval and subsequent incremental frame-to-tile retrieval. This protocol is more challenging than the standard prefix setting because the system cannot assume that the video starts from the beginning of a trajectory, and the available observation may contain limited or visually ambiguous context. Table 3 shows that X2Localizer consistently outperforms GAReT across all random-start budgets. The improvement is largest under the single-frame restart setting, where temporal context is almost absent. These results suggest that the proposed asymmetric cross-grained alignment learns representations that are less dependent on complete sequence context. By combining budget-aware global supervision with token-aggregated local evidence, X2Localizer can rapidly re-establish reliable coarse candidates and improve subsequent frame-level localization after a restart. Method R@1/5/10/1% Prefix Coarse Retrieval GAReT 20.0/46.9/58.9/94.0 X2Localizer 24.5/55.0/66.9/97.3 Incremental Fine-Grained Retrieval GAReT 35.6/51.4/56.2/61.2 X2Localizer 39.0/57.5/63.1/69.3 (a) Random 1 frame (τ=1τ=1). Method R@1/5/10/1% Prefix Coarse Retrieval GAReT 29.1/60.5/71.7/97.4 X2Localizer 32.7/65.2/76.2/98.8 Incremental Fine-Grained Retrieval GAReT 41.5/61.0/67.6/74.5 X2Localizer 43.1/64.0/70.9/78.5 (b) Random 5s clip (τ=2τ=2). Method R@1/5/10/1% Prefix Coarse Retrieval GAReT 36.6/70.6/79.9/98.7 X2Localizer 38.3/73.2/83.6/99.4 Incremental Fine-Grained Retrieval GAReT 43.9/66.2/74.1/83.1 X2Localizer 44.8/68.4/76.9/86.1 (c) Random 20s clip (τ=4τ=4). Table 3: Random-start recovery. The system is initialized from a random frame or clip, and we evaluate both coarse and subsequent incremental frame-level retrieval. 4.5 Long-distance Progressive Localization We further evaluate long-range deployment on the constructed long-distance subset. In this setting, two temporally adjacent sequences from the same geographic source are concatenated to simulate continuous localization over an extended route. This setting is challenging because the initially retrieved aerial region may become outdated as the trajectory moves into a new area. We compare four configurations: GAReT without SWRL, GAReT with SWRL, X2Localizer without SWRL, and X2Localizer with SWRL. Without SWRL, the system performs coarse retrieval only once and then applies frame-level retrieval using the initial candidate gallery. With SWRL, the candidate region is periodically refreshed using a 5-second sliding-window prefix, enabling short warm-start and online re-localization. Frame-level retrieval is evaluated at key frames sampled approximately every second. The performance curves in Fig. 4 reveal a clear limitation of the conventional full-video CVG paradigm. Although GAReT without SWRL performs well near the initial segment, its accuracy drops when the trajectory moves beyond the initially retrieved region. SWRL alleviates this issue by periodically updating the coarse candidate gallery, allowing the system to adapt to cross-region transitions without reprocessing the entire video. X2Localizer further improves over GAReT under the same SWRL protocol, demonstrating that the proposed asymmetric cross-grained alignment provides more reliable short-prefix coarse retrieval. These results indicate that progressive re-localization and cross-grained alignment are complementary: SWRL supplies the online update mechanism, while X2Localizer improves the quality of each short-budget re-localization step. Figure 4: Long-range localization on the challenging subset. We compare our proposed method against GAReT, both with and without SWRL, which supports short warm-start and periodic re-localization via SWRL. Dashed lines indicate warm-up and backward refinement performance, whereas solid lines denote incremental frame-level refinement after activation. Figure 5: Qualitative visualization of frame-level localization cues. We show representative key frames, the corresponding aerial reference image, and the heatmaps produced by GAReT and X2Localizer. X2Localizer yields sharper and more temporally consistent responses across key frames, suggesting that asymmetric cross-grained alignment helps aggregate reliable local evidence for progressive localization. 4.6 Ablation Study Table 4(a) validates the proposed asymmetric cross-grained objective. Using only ℒg(τ)L_g^(τ) gives strong long-prefix performance but is weaker for short observations, while using only ℒf(τ)L_f^(τ) improves the single-frame case but hurts longer budgets. This shows that global and fine-grained alignment are complementary. The symmetric variant with λf=λg _f= _g performs worse, especially at τ=8τ=8, confirming the need for budget-dependent weighting. Removing either ℒselfL_ self or ℒteacherL_ teacher reduces the average score, supporting both ranking regularizers. Table 4(b) studies SWRL under the most challenging single-frame setting. Compared with fixed initial candidates, SWRL improves fine-grained retrieval by refreshing the candidate region with a sliding-window prefix. Using smix(τ)s_mix^(τ) outperforms sg(τ)s_g^(τ), showing that cross-grained similarity produces better re-localization candidates. With Kc=5K_c=5, SWRL improves R@1/5/10, while R@1% slightly decreases. Qualitative Analysis. Figure 5 further visualizes the frame-level localization cues produced by GAReT and X2Localizer. Compared with the baseline, X2Localizer produces more concentrated and temporally consistent responses around the correct aerial regions. This is especially visible in visually ambiguous frames, where global video-level context alone may lead to diffuse or shifted activations. The visualization supports our quantitative findings: the proposed asymmetric cross-grained objective encourages the model to preserve local frame–tile evidence while maintaining stable prefix-level alignment, which is beneficial for progressive and short-budget localization. Variant τ=1τ=1 τ=2τ=2 τ=4τ=4 τ=8τ=8 Avg. w/o X obj. 16.9 25.9 41.4 50.2 33.6 ℒalign=ℒg(τ)L_align=L_g^(τ) only 20.3 28.6 43.2 50.4 35.6 ℒalign=ℒf(τ)L_align=L_f^(τ) only 21.3 28.5 41.6 50.7 35.5 λf=λg _f= _g (symmetric) 21.3 28.4 42.3 47.2 34.8 w/o Distill ℒselfL_self 20.9 28.7 42.4 49.8 35.5 w/o Distill ℒteacherL_teacher 21.6 28.6 42.5 49.3 35.5 X2Localizer (X obj.) 21.6 29.1 42.5 50.3 35.9 (a) Cross-grained objective (X obj.). Variant Sim. KcK_c Gate@KcK_c R@1/5/10/1% w/o SWRL smix(τ)s_mix^(τ) 5 50.9 34.3/48.7/52.5/55.3 SWRL smix(τ)s_mix^(τ) 5 50.9 41.4/53.5/53.5/53.5 w/o SWRL smix(τ)s_mix^(τ) 10 63.7 37.5/55.2/60.5/66.3 SWRL sg(τ)s_g^(τ) 10 61.3 43.4/60.2/62.3/62.3 SWRL smix(τ)s_mix^(τ) 10 63.7 44.9/62.2/64.5/64.5 (b) Similarity choice and SWRL design when τ=1τ=1. Table 4: Ablation studies of X2Localizer. (a) Component analysis of the asymmetric cross-grained alignment objective under different temporal budgets in Recall@1. (b) Inference-time analysis of the SWRL strategy under the single-frame setting (τ=1τ=1). Gate@KcK_c denotes whether the ground-truth global aerial region is included in the top-KcK_c coarse candidates. 5 Conclusion In this work, we revisit cross-view video geo-localization and reformulate it as Progressive Cross-view Video Geo-localization (PCVG), a deployment-oriented setting that requires localization under varying temporal budgets, arbitrary starting positions, and long-range continuous streams. To address this task, we propose X2Localizer, which learns robust prefix representations through an asymmetric cross-grained alignment objective. The objective jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching, while adapting their relative weights according to the available temporal context. We further introduce ranking distillation to preserve full-video retrieval structure and SWRL to periodically refresh candidate regions during online inference. Experiments show that X2Localizer remains competitive under the conventional full-video protocol while substantially improving short-prefix, random-start, and long-distance progressive localization. These results demonstrate the importance of combining budget-aware cross-grained alignment with online re-localization for practical cross-view video geo-localization. Acknowledgment This work was mainly supported by the Engineering and Physical Sciences Research Council through an industrial CASE studentship with Ordnance Survey (Grant number EP/W522077/1 and EP/X524840/1). This work was supported in part by the National Natural Science Foundation of China under Grant No. 62503166, in part by the Hunan Provincial Research and Development Project under Grant number 2026QK3018, in part by the Yuelushan Industrial Innovation Center, in part by the Helmholtz Association of German Research Centers, in part by the Ministry of Science, Research and the Arts of Baden-Württemberg (MWK) through the Cooperative Graduate School Accessibility through AI-based Assistive Technology (KATE) under Grant BW6-03, and in part by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT and HOREKA@KIT partitions. References Berrabah et al. (2011) S. A. Berrabah, H. Sahli, and Y. Baudoin Visual-based simultaneous localization and mapping and global positioning system correction for geo-localization of a mobile robot. Measurement Science and Technology 22 (12), p. 124003. Cited by: §1. Bertasius et al. (2021) G. Bertasius, H. Wang, and L. Torresani Is space-time attention all you need for video understanding?. In Proceedings of the 38th International Conference on Machine Learning, M. Meila and T. Zhang (Eds.), Proceedings of Machine Learning Research, Vol. 139, p. 813–824. Cited by: §4.2, Table 1. Brosh et al. (2019) E. Brosh, M. Friedmann, I. Kadar, L. Yitzhak Lavy, E. Levi, S. Rippa, Y. Lempert, B. Fernandez-Ruiz, R. Herzig, and T. Darrell Accurate visual localization for automotive applications. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, p. 0–0. Cited by: §1. Deng et al. (2009) J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei Imagenet: a large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, p. 248–255. Cited by: §4.1. Deuser et al. (2023) F. Deuser, K. Habel, and N. Oswald Sample4geo: hard negative sampling for cross-view geo-localisation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, p. 16847–16856. Cited by: §1, §2. Fervers et al. (2023) F. Fervers, S. Bullinger, C. Bodensteiner, M. Arens, and R. Stiefelhagen Uncertainty-aware vision-based metric cross-view geolocalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 21621–21631. Cited by: §2. Fervers et al. (2024) F. Fervers, S. Bullinger, C. Bodensteiner, M. Arens, and R. Stiefelhagen Statewide visual geolocalization in the wild. In European Conference on Computer Vision, p. 438–455. Cited by: §1, §2. Gadd et al. (2020) M. Gadd, D. De Martini, and P. Newman Look around you: sequence-based radar place recognition with learned rotational invariance. In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), p. 270–276. Cited by: §1, §2, §3.4. Goo et al. (2025) J. M. Goo, Z. Zeng, L. Morelli, F. Remondino, and J. Boehm Exploring modern end-to-end ai-based multi-view 3d reconstruction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 48, p. 91–97. Cited by: §1. Hou et al. (2025) Q. Hou, C. Hou, F. Zhang, and Q. Weng Crowd-sourced images geo-localization method based on multi-modal deep learning. In EGU General Assembly Conference Abstracts, p. EGU25–14877. Cited by: §1. Hussaini et al. (2025) S. Hussaini, T. Fischer, and M. Milford Improving visual place recognition with sequence-matching receptiveness prediction. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), p. 11073–11080. Cited by: §1, §2. Lentsch et al. (2023) T. Lentsch, Z. Xia, H. Caesar, and J. F. Kooij Slicematch: geometry-guided aggregation for cross-view pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 17225–17234. Cited by: §2. Liu et al. (2022) Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu Video swin transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, p. 3202–3211. Cited by: §4.2, Table 1. Ma et al. (2022) Y. Ma, G. Xu, X. Sun, M. Yan, J. Zhang, and R. Ji X-clip: end-to-end multi-grained contrastive learning for video-text retrieval. In Proceedings of the 30th ACM international conference on multimedia, p. 638–647. Cited by: §1. Milford and Wyeth (2012) M. J. Milford and G. F. Wyeth SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In 2012 IEEE international conference on robotics and automation, p. 1643–1649. Cited by: §1, §2. Milford (2013) M. Milford Vision-based place recognition: how low can you go?. The International Journal of Robotics Research 32 (7), p. 766–789. Cited by: §1, §1, §2. Pan et al. (2022) J. Pan, Z. Lin, X. Zhu, J. Shao, and H. Li St-adapter: parameter-efficient image-to-video transfer learning. Advances in Neural Information Processing Systems 35, p. 26462–26477. Cited by: §3.2. Pillai et al. (2024) M. S. Pillai, M. N. Rizve, and M. Shah Garet: cross-view video geolocalization with adapters and auto-regressive transformers. In European Conference on Computer Vision, p. 466–483. Cited by: §1, §1, §1, §2, §3.1, §3.2, §3.2, §4.2, Table 1, Table 1, Table 1, Table 1, Table 2, Table 2, Table 2, Table 2. Radford et al. (2021) A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, p. 8748–8763. Cited by: §1. Regmi and Shah (2021) K. Regmi and M. Shah Video geo-localization employing geo-temporal feature learning and gps trajectory smoothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, p. 12126–12135. Cited by: §1. Shi and Li (2022) Y. Shi and H. Li Beyond cross-view image retrieval: highly accurate vehicle localization using satellite image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 17010–17020. Cited by: §2. Shi et al. (2019) Y. Shi, L. Liu, X. Yu, and H. Li Spatial-aware feature aggregation for image based cross-view geo-localization. Advances in Neural Information Processing Systems 32. Cited by: §2, §3.2. Shi et al. (2020) Y. Shi, X. Yu, D. Campbell, and H. Li Where am i looking at? joint location and orientation estimation by cross-view matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 4064–4072. Cited by: §2, §4.3, Table 2. Shi et al. (2022a) Y. Shi, X. Yu, L. Liu, D. Campbell, P. Koniusz, and H. Li Accurate 3-dof camera geo-localization via ground-to-satellite image matching. IEEE transactions on pattern analysis and machine intelligence 45 (3), p. 2682–2697. Cited by: §2. Shi et al. (2022b) Y. Shi, X. Yu, S. Wang, and H. Li Cvlnet: cross-view semantic correspondence learning for video-based camera localization. In Asian Conference on Computer Vision, p. 123–141. Cited by: §1, §1, §2, §4.2, Table 1. Tian et al. (2025) F. Tian, M. Feng, J. Luo, Z. Wu, L. Mei, L. Yang, W. Dong, and Y. Wang Generalizing to new area: self-distillation curriculum learning for fine-grained cross view localization. In Proceedings of the 33rd ACM International Conference on Multimedia, p. 11996–12005. Cited by: §1. Tong et al. (2025) S. Tong, Z. Xia, A. Alahi, X. He, and Y. Shi GeoDistill: geometry-guided self-distillation for weakly supervised cross-view localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, p. 25357–25366. Cited by: §1. Touvron et al. (2021) H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou Training data-efficient image transformers & distillation through attention. In International conference on machine learning, p. 10347–10357. Cited by: §3.2, §4.1, Table 1, Table 1, Table 1, Table 1, Table 1, Table 1, Table 1, Table 1. Vyas et al. (2022) S. Vyas, C. Chen, and M. Shah Gama: cross-view video geo-localization. In European Conference on Computer Vision, p. 440–456. Cited by: §1, §1, §1, §2, §3.1, §4.2, §4.3, Table 1, Table 2, Table 2. Vysotska and Stachniss (2019) O. Vysotska and C. Stachniss Effective visual place recognition using multi-sequence maps. IEEE Robotics and Automation Letters 4 (2), p. 1730–1736. Cited by: §1, §2, §3.4. Xia et al. (2023) Z. Xia, O. Booij, and J. F. Kooij Convolutional cross-view pose estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (5), p. 3813–3831. Cited by: §2. Xia et al. (2022) Z. Xia, O. Booij, M. Manfredi, and J. F. Kooij Visual cross-view metric localization with dense uncertainty estimates. In European Conference on Computer Vision, p. 90–106. Cited by: §2. Xia et al. (2024) Z. Xia, Y. Shi, H. Li, and J. FP Kooij Adapting fine-grained cross-view localization to areas without fine ground truth. In European Conference on Computer Vision, p. 397–415. Cited by: §1. Yang et al. (2021a) H. Yang, X. Lu, and Y. Zhu Cross-view geo-localization with layer-to-layer transformer. Advances in Neural Information Processing Systems 34, p. 29009–29020. Cited by: §2, §4.3, Table 2. Yang et al. (2021b) J. Yang, Y. Bisk, and J. Gao Taco: token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF international conference on computer vision, p. 11562–11572. Cited by: §1. Yang et al. (2023) T. Yang, Y. Zhu, Y. Xie, A. Zhang, C. Chen, and M. Li Aim: adapting image models for efficient video action recognition. arXiv preprint arXiv:2302.03024. Cited by: §3.2. Yao et al. (2021) L. Yao, R. Huang, L. Hou, G. Lu, M. Niu, H. Xu, X. Liang, Z. Li, X. Jiang, and C. Xu Filip: fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783. Cited by: §1. Ye et al. (2025) J. Ye, H. Lin, L. Ou, D. Chen, Z. Wang, Q. Zhu, C. He, and W. Li Where am i? cross-view geo-localization with natural language descriptions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, p. 5890–5900. Cited by: §1, §2. Zeng et al. (2026a) Z. Zeng, J. M. Goo, and J. Boehm AI-based camera pose estimation on mixed aerial and ground images: a comparative study. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 49, p. 101–107. Cited by: §1. Zeng et al. (2026b) Z. Zeng, J. M. Goo, and J. Boehm Dilated superpixel aggregation for visual place recognition. IEEE Robotics and Automation Letters 11 (2), p. 2002–2009. External Links: Document Cited by: §1. Zeng et al. (2026c) Z. Zeng, J. M. Goo, J. Zheng, W. Fan, J. Zhang, R. Stiefelhagen, and J. Boehm Faster or stronger: towards flexible visual place recognition via weighted aggregation and token pruning. arXiv preprint arXiv:2605.20551. Cited by: §1. Zhang et al. (2024) X. Zhang, X. Li, W. Sultani, C. Chen, and S. Wshah GeoDTR+: toward generic cross-view geolocalization via geometric disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (12), p. 10419–10433. Cited by: §3.2. Zhang et al. (2023a) X. Zhang, X. Li, W. Sultani, Y. Zhou, and S. Wshah Cross-view geo-localization via learning disentangled geometric layout correspondence. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37, p. 3480–3488. Cited by: §3.2. Zhang et al. (2023b) X. Zhang, W. Sultani, and S. Wshah Cross-view image sequence geo-localization. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, p. 2914–2923. Cited by: §1, §1, §2. Zhang et al. (2026) Y. Zhang, E. Ke, M. Kwan, L. Fang, and M. Li Multi-frequency street-level urban noise modeling and mapping through street view and remote sensing image fusion. Computers, Environment and Urban Systems 126, p. 102401. Cited by: §1. Zheng et al. (2026a) J. Zheng, R. Dai, R. Liu, Z. Zeng, Y. Chen, F. Wang, K. Peng, K. Yang, J. Zhang, and R. Stiefelhagen RHO: robust holistic osm-based metric cross-view geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 33727–33737. Cited by: §2. Zheng et al. (2026b) J. Zheng, Y. Huang, R. Dai, R. Liu, Y. Chen, K. Peng, K. Yang, J. Zhang, G. Wang, O. Wysocki, and R. Stiefelhagen SeqLoc: beyond the single frame for cross-view geo-localization in feature-sparse scenes. arXiv preprint arXiv:2608.07835. Cited by: §2. Zhu et al. (2022) S. Zhu, M. Shah, and C. Chen Transgeo: transformer is all you need for cross-view image geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 1162–1171. Cited by: §1, §1, §2, §3.2. Zhu et al. (2021) S. Zhu, T. Yang, and C. Chen Vigor: cross-view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 3640–3649. Cited by: §3.2.