activity
20242026
most citedPurify Unlearnable Examples via Rate-Constrained Variational Autoencoders

2 citations · 2 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification

Jiahao Nie, Yun Xing, Wenbin An +6

Motivated by the success of the Segment Anything Model (SAM) in promptable segmentation, recent studies leverage SAM to develop training-free solutions for few-shot segmentation, w…

cs.CV2026

E.M.Ground: A Temporal Grounding Vid-LLM with Holistic Event Perception and Matching

Jiahao Nie, Wenbin An, Gongjie Zhang +4

Despite recent advances in Video Large Language Models (Vid-LLMs), Temporal Video Grounding (TVG), which aims to precisely localize time segments corresponding to query events, rem…

cs.LG2026

CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning

Ronghao Lin, Qiaolin He, Sijie Mai +5

Multimodal machine learning, mimicking the human brain's ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly pai…

cs.RO2025

MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation

Runhao Li, Wenkai Guo, Zhenyu Wu +5

Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these mode…

cs.AI2025

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model

Ronghao Lin, Shuai Shen, Weipeng Hu +5

Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved te…

cs.CV2025

Towards Generalized Range-View LiDAR Segmentation in Adverse Weather

Longyu Yang, Lu Zhang, Jun Liu +4

LiDAR segmentation has emerged as an important task to enrich scene perception and understanding. Range-view-based methods have gained popularity due to their high computational ef…