most citedMeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation

11 citations · 11 across the 6 of their papers we have counts for

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cs.CV2026

VLANeXt: Recipes for Building Strong VLA Models

Xiao-Ming Wu, Bin Fan, Kang Liao +6

Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understanding from Vision-Language Models for gen…

cs.CV2026

LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition

Muxin Pu, Mei Kuan Lim, Chun Yong Chong +1

Skeleton-based isolated sign language recognition (ISLR) demands fine-grained understanding of articulated motion across multiple spatial scales, from subtle finger movements to gl…

cs.CV2026

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

Shuai Liu, Shulin Tian, Kairui Hu +6

Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe dat…

cs.CV2026

Sigma: Semantically Informative Pre-training for Skeleton-based Sign Language Understanding

Muxin Pu, Mei Kuan Lim, Chun Yong Chong +1

Pre-training has proven effective for learning transferable features in sign language understanding (SLU) tasks. Recently, skeleton-based methods have gained increasing attention b…

cs.CV202511 cited

MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation

Henghui Ding, Chang Liu, Shuting He +4

This paper proposes a large-scale multi-modal dataset for referring motion expression video segmentation, focusing on segmenting and tracking target objects in videos based on lang…

cs.CV2025

MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook

Peng Xu, Shengwu Xiong, Jiajun Zhang +125

This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…