GradCO is a new method for Text-based Person Anomaly Search (TPAS) that addresses two key challenges: gradient conflict from same-identity, different-behavior hard negatives and the dominance of strong appearance cues over weak behavioral evidence. It introduces a Behavior-Aware Mixture-of-Experts (B-MoE) to separate appearance, normal-behavior, and anomalous-behavior streams, Conflict Offload Learning (COL) to reduce conflicting gradients via pair-conditioned anchors, and Hierarchical Intent Alignment (HIA) to aggregate expert similarities with query-conditioned weights. Experiments on five TPAS/TIPR benchmarks demonstrate that GradCO significantly outperforms state-of-the-art methods, validating its design choices.
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.
Text-based person retrieval faces a critical but under-explored challenge: the inherent uncertainty of query granularity in real-world scenarios. This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity, and provides a systematic solution.
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...
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.
Text-based person anomaly search requires a model to retrieve individuals from a large image gallery using natural-language descriptions of both appearance and behavior. This task is challenging becau...
arXiv:2602. 07218v2 Announce Type: replace-cross Abstract: Adaptability has been regarded as a central feature in the foundation models, enabling them to effectively acclimate to unseen downstream tasks.
arXiv:2608.27997v1 Announce Type: new Abstract: Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intelli...
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...
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...
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.
The paper introduces HFCMA, a hierarchical fine-grained cross‑modal alignment framework that transforms low‑level image patches and text tokens into adaptive visual regions and textual phrases through dynamic granularity discovery. It then performs structured region‑phrase matching using an entropy‑regularized optimal transport formulation, incorporating background debiasing, textual importance weighting, and prototype consistency regularization. Experiments on Flickr30K and MS‑COCO demonstrate that this approach improves fine‑grained image‑text retrieval by providing structured local alignment information and better discrimination of subtle semantic differences.
The paper introduces Grounded Human-Attributed Description and Activity Recognition (GHADAR), a task that requires describing the characteristics and activities of every person in a video given their locations. It presents a new dataset derived from AVA-Actions with open‑set captions, a method that constrains cross‑attention masks in vision‑language models to better use grounding information, and a holistic VLM‑based evaluation schema that compares concepts directly between video and predictions. Experiments show the proposed method surpasses state‑of‑the‑art VLMs and highlight limitations of existing evaluation metrics.