Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval

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

arXiv · 2608.12843

Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval

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

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Abstract

Text-based person anomaly retrieval aims to retrieve pedestrians exhibiting anomalous behaviors from a large image gallery using natural language descriptions. Compared with conventional text-based person retrieval, this task requires fine-grained reasoning over pedestrian appearance, behaviors, object interactions, and scene context, making robust cross-modal matching significantly more challenging. This paper presents the GENAI4E team's solution to AI City Challenge 2026 Track 4. Our framework builds upon a strong retrieval backbone and progressively integrates heterogeneous vision-language embedding models through score alignment and iterative ensemble fusion, followed by disagreement-aware VLM reranking for ambiguous queries. On the official Pedestrian Anomaly Behavior (PAB) benchmark, our approach achieves 90.92% mAP, 85.13% Recall@1, 97.72% Recall@5, and 98.68% Recall@10, demonstrating the effectiveness of combining complementary vision-language representations with selective multimodal reasoning for large-scale text-based person anomaly retrieval.

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BibTeX RIS

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. 2026-08-13. Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval. https://arxiv.org/abs/2608.12843

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