collaborators

6 papers

cs.RO2026

SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation

Changyuan Wang, Chubin Zhang, Zhenyu Wu +8

Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. How…

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.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…