The paper introduces a multi‑model retrieval and reranking system for text‑based person anomaly search that bridges the synthetic‑to‑real domain gap. It adapts the Qwen3‑VL‑Embedding‑8B model to synthetic descriptions via contrastive learning and domain randomization, fuses scores from six Qwen3‑VL checkpoints and a frozen SigLIP2 model, and then reranks candidates with a Qwen3‑VL‑Reranker‑8B cross‑encoder. A synthetic distractor‑matched validation protocol is used to tune hyper‑parameters, yielding a 94.78% mAP and 90.85% R@1 on the AI City Challenge 2026 Track 4 test set, placing eighth on the public leaderboard.
By Eugene Denteh, Blessing Agyei Kyem, Andrews Danyo, Joshua Kofi Asamoah, Richard Dzinyela, Solomon Owusu-Ansah, Dickson G. Asiedu, Armstrong Aboah
arXiv:2608.23503v1 Announce Type: new
Abstract: Text-based person anomaly search requires distinguishing individuals based on fine-grained, context-dependent behaviors rather than mere appearance. Ex...
By Thanh-Khoi Nguyen, Thanh-Nhan Vo, Trong-Thuan Nguyen, Minh-Triet Tran
arXiv:2608. 12843v1 Announce Type: cross Abstract: Text-based person anomaly retrieval aims to retrieve pedestrians exhibiting anomalous behaviors from a large image gallery using natural language descriptions.
By Huu-An Vu, Cam Tu Tran Thi, Thanh Toan Le Ngo, Hoang Vo, Do Trung Hieu, Hieu Dinh Trung Pham, Khang Minh Le, Huy Minh Nhat Nguyen
SCOUT is a frozen‑encoder approach for sim‑to‑real text‑based person retrieval that predicts cross‑modal embeddings instead of fine‑tuning cross‑encoders. It uses a trainable predictor to map patch tokens from a frozen video encoder (V‑JEPA) into the embedding space of a frozen text encoder (EmbeddingGemma), guided by a bidirectional InfoNCE objective. The method achieves state‑of‑the‑art results on the AI City Challenge 2026 Track 4, with a full retrieve‑fuse‑rerank pipeline reaching 84.25 mAP@10 and a single frozen model alone scoring 60.63, while training costs are modest (≈95 GPU‑hours).
By Abdarahmane Traor\'e, Andy Couturier, \'Eric Hervet
The paper introduces PRAISE, a test‑time adaptation framework for vision‑language model based person re‑identification. PRAISE constructs an Attribute‑Conditioned Subspace (ACS) from a bank of attribute prompts, reshaping query‑gallery affinities without requiring identity or attribute labels. Experiments on image‑to‑image and text‑to‑image ReID benchmarks show that PRAISE consistently improves retrieval performance over existing backbones and TTA baselines.
By Rajarshi Bhattacharya, Aryan Shukla, Shakeeb Murtaza, Christian Desrosiers, Jose Dolz, Maguelonne Heritier, Eric Granger
arXiv:2610.10163v1 Announce Type: new
Abstract: Linking people's appearance and actions to character identities is essential for understanding video narratives. We present a framework for identity-aw...
By Anas Filali Razzouki, Killian Steunou, Khalil Guetari, Thomas Kling, Moun\^im El-Yacoubi, Yannis Tevissen
arXiv:2609.14419v1 Announce Type: cross
Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chan...
By Leon Fernando, C Dombawala, P. Hettigoda, Vanodhya G. Warnasooriya, Ishara Neranjana, Rashmika Nawaratne
ReText is a method that enhances image-based person re-identification by incorporating textual descriptions into training. The authors augment existing multi-camera datasets with captions generated by a fine‑tuned Vision‑Language Model and train the system on three tasks: Re-ID, image‑text matching, and image reconstruction guided by text. Experiments show that ReText improves generalization across unseen domains and outperforms current state‑of‑the‑art approaches on cross‑domain Re‑ID benchmarks.
By Timur Mamedov, Karina Kvanchiani, Anton Konushin, Vadim Konushin
arXiv:2606. 02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations.
By Karina Kvanchiani, Timur Mamedov
arXiv:2609.10224v1 Announce Type: new
Abstract: Vision-language models such as CLIP embed images and text in a shared space, where modality-specific distributions often remain separated. Existing acc...
By Zonglin Yang, Huilan Ma, Xudan Zheng, Yuejun Xie
arXiv:2609.12965v1 Announce Type: new
Abstract: Text-based person search (TBPS) aims to retrieve images of a target person from a large image gallery based on a given natural language description. Mo...
By Mang Ye, Yucheng Ji, Yang Bai, Min Cao, Siyuan Chai, Bo Du, Min Zhang
arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.
By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim