activity
20242026
most citedLearning Motion and Temporal Cues for Unsupervised Video Object Segmentation

12 citations · 18 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Towards Cross-Platform Generalization: Domain Adaptive 3D Detection with Augmentation and Pseudo-Labeling

Xiyan Feng, Wenbo Zhang, Lu Zhang +3

This technical report represents the award-winning solution to the Cross-platform 3D Object Detection task in the RoboSense2025 Challenge. Our approach is built upon PVRCNN++, an e…

cs.RO2026

The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms

Lingdong Kong, Shaoyuan Xie, Zeying Gong +135

Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…

cs.HC2025

Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novels

Yifei Huang, Tianyu Yan, Sitong Gong +5

We present the Living Novel, an end-to-end system that transforms any literary work into an immersive, multi-character conversational experience. This system is designed to solve t…

cs.CV2025

Parameter Aware Mamba Model for Multi-task Dense Prediction

Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong +3

Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attenti…

cs.CV2025

FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning

Lu Zhang, Jiazuo Yu, Haomiao Xiong +4

Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs f…

cs.CV202512 cited

Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation

Yunzhi Zhuge, Hongyu Gu, Lu Zhang +2

In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and…