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

2 citations · 5 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.CV2026

TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons

Qichen Zheng, Siyuan Yang, Chong Wang +4

Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically bu…

cs.CV2026

Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models

Siyuan Yang, Jun Liu, Hao Cheng +5

Recent advances in large-scale pretrained vision models have demonstrated impressive capabilities across a wide range of downstream tasks, including cross-modal and multi-modal sce…

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.CV2026

Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation

Jiahao Nie, Guanqiao Fu, Wenbin An +3

Cross-Domain Few-Shot Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-…