activity
20232026
most citedInterpreting and Improving Attention From the Perspective of Large Kernel Convolution

1 citations · 1 across the 17 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.CV2025

ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

Wencheng Ye, Tianshi Wang, Lei Zhu +3

Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational…

cs.AI2025

BLM: A Boundless Large Model for Cross-Space, Cross-Task, and Cross-Embodiment Learning

Wentao Tan, Bowen Wang, Heng Zhi +15

Multimodal large language models (MLLMs) have advanced vision-language reasoning and are increasingly deployed in embodied agents. However, significant limitations remain: MLLMs ge…

cs.LG2025

Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation

Xiaofeng Cao, Mingwei Xu, Xin Yu +8

Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain…

cs.CV2025

Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation

Longzhen Yang, Zhangkai Ni, Ying Wen +3

Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images, anchored in explicit visual evidence to improve interpretability and f…

cs.DB2025

SINDI: An Efficient Index for Sparse Vector Approximate Maximum Inner Product Search

Ruoxuan Li, Xiaoyao Zhong, Jiabao Jin +7

Sparse vector Maximum Inner Product Search (MIPS) is crucial in multi-path retrieval for Retrieval-Augmented Generation (RAG). Recent inverted index-based and graph-based algorithm…

cs.CV2025

Unified modality separation: A vision-language framework for unsupervised domain adaptation

Xinyao Li, Jingjing Li, Zhekai Du +2

Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains. Recently, pre-trained vision-language models (VLMs) have dem…

cs.CV2025

Implicit Counterfactual Learning for Audio-Visual Segmentation

Mingfeng Zha, Tianyu Li, Guoqing Wang +4

Audio-visual segmentation (AVS) aims to segment objects in videos based on audio cues. Existing AVS methods are primarily designed to enhance interaction efficiency but pay limited…