activity
20152026
most citedVisual Saliency Detection Based on Multiscale Deep CNN Features

416 citations · 1.8k across the 110 of their papers we have counts for

collaborators

113 papers

cs.RO2026

FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation

Ganlong Zhao, Zijia Tang, Xingping Chen +3

Vision-Language-Action Models~(VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks. However, their performance remains b…

cs.CV2026

RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models

Yuwei Ning, Liangzhi Wang, Yi Xiao +5

Realistic weather translation is valuable for developing and evaluating autonomous driving systems, yet collecting paired videos of the same scenes under different weather conditio…

cs.RO2026

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

Xiaomeng Fu, Junfan Lin, Yang Liu +4

Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and…

cs.CL2026

Bridging the Agent-World Gap: Text World Models for LLM-based Agents

Yixia Li, Hongru Wang, Peng Lai +13

Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet m…

cs.CV2026

RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos

Zhenhua Wu, Yun Pang, Mingkun Chang +4

Long-tail hazardous scenarios are essential for safety-oriented autonomous driving, yet they are difficult to collect at scale. Editable 3D Gaussian Splatting (3DGS) simulation off…

cs.CV2025

Free-MoRef: Instantly Multiplexing Context Perception Capabilities of Video-MLLMs within Single Inference

Kuo Wang, Quanlong Zheng, Junlin Xie +6

Video Multimodal Large Language Models~(Video-MLLM) have achieved remarkable advancements in video understanding tasks. However, constrained by the context length limitation in the…