29 citations · 32 across the 20 of their papers we have counts for
8 papers · 1 filter
Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
Haijing Liu, Zhiyuan Song, Hefeng Wu +3
Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-pe…
Massive Editing for Large Language Models Based on Dynamic Weight Generation
Wentao Wan, Qiqing Lao, Zhiwei Xie +4
Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-sc…
GeoRemover: Removing Objects and Their Causal Visual Artifacts
Zixin Zhu, Haoxiang Li, Xuelu Feng +3
Towards intelligent image editing, object removal should eliminate both the target object and its causal visual artifacts, such as shadows and reflections. However, existing image…
DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +2
Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image, requiring both precise intra-class localization to pin…
RoboPearls: Editable Video Simulation for Robot Manipulation
Tao Tang, Likui Zhang, Youpeng Wen +9
The development of generalist robot manipulation policies has seen significant progress, driven by large-scale demonstration data across diverse environments. However, the high cos…
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
Zhongzhan Huang, Guoming Ling, Shanshan Zhong +2
Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existi…
RoBridge: A Hierarchical Architecture Bridging Cognition and Execution for General Robotic Manipulation
Kaidong Zhang, Rongtao Xu, Pengzhen Ren +4
Operating robots in open-ended scenarios with diverse tasks is a crucial research and application direction in robotics. While recent progress in natural language processing and la…
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
Zhongzhan Huang, Guoming Ling, Yupei Lin +4
Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input. In this paper, our comprehensive an…