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.
By Quentin Dh\^ote, Romaric Audigier
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.
By Yu Wang, Hongyu Yang
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...
By Yu Wang, Hongyu Yang
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: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.
By Kaicong Huang, Talha Azfar, Jack Reilly, Ruimin Ke
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.
By Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani, Homayoun Najjaran
arXiv:2610.33641v1 Announce Type: new
Abstract: Attribute-based person retrieval ranks surveillance images from a binary query of semantic attributes, but mean Average Attribute Degree of Match (mADM...
By Nguyen Dang Duy Tan, Vo Thanh Nguyen, Linh T. T. Tran, Huynh Nguyen Phat
arXiv:2609.25500v1 Announce Type: new
Abstract: Training data quantity and quality greatly affect object detection model performance, regardless of model architecture. When using object detection mod...
By Lonny Lundsten, Kevin Barnard, Dave Caress
The paper investigates why visual instance retrieval often fails when the same object appears at different apparent sizes in query and gallery images. It identifies object-to-image (O2I) ratio mismatch—where the object occupies different fractions of the two images—as the dominant cause of performance degradation, rather than resolution loss. Experiments on a controlled benchmark with 3,021 Objaverse objects show that over 80% of cross-distance degradation is due to O2I mismatch for most pretrained backbones, and that multi-scale architectures only reduce resolution effects but remain vulnerable to O2I mismatch. The authors demonstrate that query-side scale augmentation and an OWLv2 crop reranker achieve state‑of‑the‑art results on ILIAS 100M without retraining the gallery, and that a LoRA fine‑tune can match these gains in a single forward pass, indicating that O2I robustness can be learned.
By Boaz Meivar, Ofir Kedem, Amit Edenzon, Gal Chechik, Shai Avidan
The paper investigates how the choice of retrieval encoder affects cache‑based test‑time adaptation for CLIP. By keeping the memory fixed and varying the retrieval space across sixteen encoders, the authors show that retrieval space can dramatically alter performance, with gains ranging from +0.44 to +19.7 points on ImageNet‑A. They introduce MARC, a training‑free system that pairs frozen CLIP with DINOv2‑B for retrieval, achieving superior out‑of‑distribution accuracy and efficiency compared to prior methods.
By Mahir Shahriar Tamim, Md. Samiul Alim, Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Meharun Nesa, Mohammad Abu Yousuf, Alex Lamb, Mohammad Ali Moni
MINER is a training‑free inference framework that enhances frozen dual‑encoder models for text‑to‑image retrieval when queries refer to small, visually subordinate objects in cluttered scenes. It augments the global image embedding with a bank of region‑level embeddings and applies hubness‑correcting similarity rescoring, thereby recovering visual evidence that global pooling underweights. The authors introduce ROCS, a benchmark derived from Flickr30K and MS COCO, and demonstrate that MINER improves retrieval performance across CLIP, SigLIP, and SigLIP 2 backbones on both ROCS and standard splits, attributing gains mainly to broader spatial coverage rather than precise crop placement.
By Abdulmalik Alquwayfili, Faisal AlMeshal, Jumanah Almajnouni, Huda Abdulhadi Alamri, Muhammad Kamran J Khan
arXiv:2608.29733v1 Announce Type: new
Abstract: Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically disti...
By Gonglin Chen, Ben Southall, Hanyuan Xiao, Wenbin Teng, Haolin Xiong, Tianwen Fu, Junyi Ouyang, Kshitij Singh Minhas, Supun Samarasekera, Rakesh Kumar, Yajie Zhao