11 citations · 41 across the 16 of their papers we have counts for
16 papers
DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning
Abrar Majeedi, Zhiyuan Ruan, Ziyi Zhao +3
Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain fragile under common degrad…
RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
Tianxing Chen, Yuran Wang, Mingleyang Li +16
Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle…
User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
Hongcheng Wang, Jinyu Zhu, Hao Dong
In the evolving field of robotics, the challenge of Object Navigation (ON) in household environments has attracted significant interest. Existing ON benchmarks typically place obje…
Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback
Jianglin Lu, Yuanwei Wu, Ziyi Zhao +4
Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration…
Why Tree-Style Branching Matters for Thought Advantage Estimation in GRPO
Hongcheng Wang, Yinuo Huang, Sukai Wang +2
Group Relative Policy Optimization (GRPO) trains Chain-of-Thought reasoning with verifiable rewards, but estimating thought-level advantages without value functions often suffers f…
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-object Demand-driven Navigation
Hongcheng Wang, Peiqi Liu, Wenzhe Cai +3
The process of satisfying daily demands is a fundamental aspect of humans' daily lives. With the advancement of embodied AI, robots are increasingly capable of satisfying human dem…
InstructNav: Zero-shot System for Generic Instruction Navigation in Unexplored Environment
Yuxing Long, Wenzhe Cai, Hongcheng Wang +2
Enabling robots to navigate following diverse language instructions in unexplored environments is an attractive goal for human-robot interaction. However, this goal is challenging…
SynCDR : Training Cross Domain Retrieval Models with Synthetic Data
Samarth Mishra, Carlos D. Castillo, Hongcheng Wang +2
In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model need…
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Hongcheng Wang, Andy Guan Hong Chen, Xiaoqi Li +2
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. In order to successfully accomplish the VON task, two ess…
Lightweight Delivery Detection on Doorbell Cameras
Pirazh Khorramshahi, Zhe Wu, Tianchen Wang +2
Despite recent advances in video-based action recognition and robust spatio-temporal modeling, most of the proposed approaches rely on the abundance of computational resources to a…
Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation
Hongcheng Wang, Yuxuan Wang, Fangwei Zhong +4
Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, \emph{e.g.}, household robots and rescue robots. In th…
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks
Haoqi Yuan, Chi Zhang, Hongcheng Wang +4
We study building multi-task agents in open-world environments. Without human demonstrations, learning to accomplish long-horizon tasks in a large open-world environment with reinf…
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
Tianhao Wu, Fangwei Zhong, Yiran Geng +4
Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined obje…
VideoSSL: Semi-Supervised Learning for Video Classification
Longlong Jing, Toufiq Parag, Zhe Wu +2
We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised…
Layout-induced Video Representation for Recognizing Agent-in-Place Actions
Ruichi Yu, Hongcheng Wang, Ang Li +3
We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We…
ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
Ruichi Yu, Hongcheng Wang, Larry S. Davis
This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional met…