arXiv Computer Vision
BagDINO: Multi-View Baggage Re-Identification with DINOv3
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arXiv AI
Oct 31
Luggage re-identification: A study of strategies towards a real-world application
The paper investigates strategies for luggage re-identification (ReID) in real-world video surveillance settings, such as airports and border crossings. It focuses on an end-to-end pipeline that processes multiple video streams to detect and re-identify luggage, addressing challenges like occlusions, cluttered backgrounds, and crowded scenes. The study evaluates different approaches for selecting and merging multiple views and shots of the same luggage, as well as training on varied environment datasets, to improve overall ReID performance in practical applications.
arXiv Machine Learning
Jul 27
Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era
arXiv:2607. 22068v1 Announce Type: cross Abstract: Multi-branch architectures and CNN-Transformer fusion have long been regarded as effective ways to improve vehicle re-identification (Re-ID) by combining complementary representations.
arXiv Machine Learning
Sep 22
Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification under Foundation-Model Pretraining
arXiv:2607.22068v2 Announce Type: replace-cross Abstract: Multi-branch architectures and CNN-Transformer fusion are widely believed to improve vehicle re-identification (Re-ID) by combining complemen...
Hugging Face Trending Papers
Sep 20
Retrieval Geometry Shapes Cache-Based Clip Adaptation
Cache-based test-time adaptation improves CLIP predictions by storing and retrieving examples from the target stream while keeping the model frozen. However, existing methods largely treat the feature...
arXiv AI
Aug 12
TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification
arXiv:2504. 11500v3 Announce Type: replace-cross Abstract: Transit Origin-Destination (OD) data are fundamental for optimizing public transit services, yet current collection methods, such as manual surveys, Bluetooth/WiFi tracking, and Automated Passenger Counters, are often costly, device-dependent, or unable to support individual-level matching.
arXiv AI
Oct 1
Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
The paper introduces TSDA-Track, a Template-Search Domain Adaptation framework designed to reduce modality gaps in cross‑modal visual object tracking. Two variants are explored: Pre‑AFA TSDA‑Track uses adversarial alignment before transformer interaction, while Enc‑CFA TSDA‑Track applies contrastive alignment after interaction to strengthen cross‑modal correspondence. Experiments on datasets such as LasHeR, RGBT234, GTOT, and Anti‑UAV‑024 show that both variants outperform state‑of‑the‑art trackers, with Pre‑AFA achieving an SR/PR of 43.2/56.0 on RGBT234 under the modality‑switch protocol.