collaborators

7 papers

cs.IR2026

Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval

Runhao Li, Xiaoxu Ma, Zhenyu Weng +5

Unsupervised cross-modal hashing enables efficient retrieval of semantically related instances across different modalities without requiring manual semantic annotation. However, ex…

cs.IR2026

Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing

Runhao Li, Xiaoxu Ma, Zhenyu Weng +5

Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existi…

cs.CV2026

UniHash: Unifying Pointwise and Pairwise Hashing Paradigms

Xiaoxu Ma, Runhao Li, Xiangbo Zhang +1

Effective retrieval across both seen and unseen categories is crucial for modern image retrieval systems. Retrieval on seen categories ensures precise recognition of known classes,…

cs.CL2026

Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations

Xiaoxu Ma, Xiangbo Zhang, Zhenyu Weng

Evaluating personality traits in Large Language Models (LLMs) is key to model interpretation, comparison, and responsible deployment. However, existing questionnaire-based evaluati…

cs.RO2025

MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation

Runhao Li, Wenkai Guo, Zhenyu Wu +5

Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these mode…

cs.CV2025

Mutual Learning for Hashing: Unlocking Strong Hash Functions from Weak Supervision

Xiaoxu Ma, Runhao Li, Zhenyu Weng

Deep hashing has been widely adopted for large-scale image retrieval, with numerous strategies proposed to optimize hash function learning. Pairwise-based methods are effective in…