2 citations · 3 across the 19 of their papers we have counts for
7 papers · 1 filter
Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation
Runzhong Zhang, Yueqi Duan, Yang Chen +4
Timestamp-supervised action segmentation aims to segment and classify actions in untrimmed videos with a random frame annotated per action. Precisely localizing action boundaries f…
OneHOI: Unifying Human-Object Interaction Generation and Editing
Jiun Tian Hoe, Weipeng Hu, Xudong Jiang +2
Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split int…
Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting
Yang Chen, Yi Yu, Jiaming He +3
Recent advances in 3D Gaussian Splatting (3DGS) deliver high-quality rendering, yet the Gaussian representation exposes a new attack surface, the resource-targeting attack. This at…
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…
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…
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…
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-…