3 citations · 7 across the 9 of their papers we have counts for
10 papers
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection
Xu Lin, Jinlong Peng, Zhenye Gan +2
Existing Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on st…
TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition
Yanan Liu, Jun Liu, Hao Zhang +4
Skeleton-based action recognition has garnered significant attention in the computer vision community. Inspired by the recent success of the selective state-space model (SSM) Mamba…
Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
Jingmin Zhu, Anqi Zhu, Hossein Rahmani +3
We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization…
YOLOA: Real-Time Affordance Detection via LLM Adapter
Yuqi Ji, Junjie Ke, Lihuo He +5
Affordance detection aims to jointly address the fundamental "what-where-how" challenge in embodied AI by understanding "what" an object is, "where" the object is located, and "how…
DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition
Jingmin Zhu, Anqi Zhu, James Bailey +5
Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantic…
Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds
Xianhui Meng, Yukang Huo, Li Zhang +6
Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored p…
LEAD: Exploring Logit Space Evolution for Model Selection
Zixuan Hu, Xiaotong Li, Shixiang Tang +3
The remarkable success of pretrain-then-finetune paradigm has led to a proliferation of available pre-trained models for vision tasks. This surge presents a significant challenge i…
From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models
Tianqin Li, Ziqi Wen, Leiran Song +3
Human vision organizes local cues into coherent global forms using Gestalt principles like closure, proximity, and figure-ground assignment -- functions reliant on global spatial s…
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation
Yuheng Xue, Nenglun Chen, Jun Liu +1
Zero-shot 3D part segmentation is a challenging and fundamental task. In this work, we propose a novel pipeline, ZeroPS, which achieves high-quality knowledge transfer from 2D pret…
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Jun Liu, Amir Shahroudy, Dong Xu +1
3D action recognition - analysis of human actions based on 3D skeleton data - becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recen…