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
The paper investigates the conflict between image‑based (I2I) and text‑based (T2I) person re‑identification objectives when trained on a shared vision encoder. It shows that optimizing both simultaneously degrades performance because I2I contracts intra‑identity distances while T2I preserves them. A two‑stage pipeline—first training the encoder for I2I invariance and then freezing it while aligning the text encoder—improves T2I results, suggesting a path toward unified ReID systems.
By Karina Kvanchiani, Timur Mamedov
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: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: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:2606. 09362v1 Announce Type: cross Abstract: Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cyclists are associated across time, frames, or camera views using learned appearance embeddings, often complemented by motion, geometric, or multimodal cues.
By Eduardo Borges, Manuel Abreu, Lu\'is Garrote, Urbano J. Nunes
The paper introduces EC²Face, a multimodal face synthesis framework that enhances semantic alignment by combining Explicit Conditional Consistency Guidance (ECCG) and Long‑Tail Adaptive Flow Matching (LAFM). ECCG enforces pixel‑level consistency between generated faces, textual descriptions, and semantic masks, while a temporal dynamic modulation adjusts supervision strength over diffusion timesteps. LAFM reweights spatial optimization signals according to attribute frequency, improving rare attribute synthesis without adding inference overhead. Experiments demonstrate that EC²Face outperforms baselines, achieving a 29.38% improvement in mask accuracy for rare attributes.
By Yushe Cao, Xuechao Zou, Xing Xi, Dianxi Shi, Chun Yu, Junliang Xing
arXiv:2610.33639v1 Announce Type: new
Abstract: Pedestrian Attribute Recognition (PAR) predicts semantic attributes of individuals, such as gender, age, clothing and accessories, and its descriptions...
By Nguyen Dang Duy Tan, Linh T. T. Tran, Huynh Nguyen Phat, Vo Thanh Nguyen
UniFGVC introduces a training‑free framework for few‑shot fine‑grained visual classification that reframes the task as a multimodal retrieval problem. It uses a Category‑Discriminative Visual Captioner to generate attribute‑aware textual descriptions, then matches image‑description pairs with category templates via pre‑trained encoders. Experiments on 12 datasets show that UniFGVC consistently outperforms existing CLIP‑based and fully‑supervised MLLM methods, demonstrating the effectiveness of the retrieval paradigm.
By Haiyun Guo, Hongyu Guo, Xiangzhao Hao, Jiarui Guo, Jinqiao Wang, Tat-Seng Chua
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. While I2I ReID focuses on identity-level invariance across images of the same person, T2I ReID is driven by instance-specific textual descriptions tied to unique visual traits.
arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.
By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh
arXiv:2609.21522v1 Announce Type: new
Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representation...
By Hang Cheng, Yan Chen, Mingyu Fan, Long Zeng