81 citations · 125 across the 13 of their papers we have counts for
15 papers
GTR: Gated Token Recurrence for Efficient Dense Prediction
Zhe Feng, Longfei Liu, Wei Liu +7
Self-attention-based vision backbones perform well on dense prediction, but the quadratic computational cost of global softmax attention limits their efficiency as image resolution…
EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
Longfei Liu, Yongjie Hou, Yang Li +7
Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory budgets. In practice, lightweight s…
SKEL-CF: Coarse-to-Fine Biomechanical Skeleton and Surface Mesh Recovery
Da Li, Jiping Jin, Xuanlong Yu +6
Parametric 3D human models such as SMPL have driven significant advances in human pose and shape estimation, yet their simplified kinematics limit biomechanical realism. The recent…
HTR-VT: Handwritten Text Recognition with Vision Transformer
Yuting Li, Dexiong Chen, Tinglong Tang +1
We explore the application of Vision Transformer (ViT) for handwritten text recognition. The limited availability of labeled data in this domain poses challenges for achieving high…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…
Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
Weihuang Liu, Xi Shen, Haolun Li +4
Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model…
SURE: SUrvey REcipes for building reliable and robust deep networks
Yuting Li, Yingyi Chen, Xuanlong Yu +2
In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability. Our investigation re…
LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation
Yihao Zhi, Xiaodong Cun, Xuelin Chen +4
Gestures are non-verbal but important behaviors accompanying people's speech. While previous methods are able to generate speech rhythm-synchronized gestures, the semantic context…
Explicit Visual Prompting for Universal Foreground Segmentations
Weihuang Liu, Xi Shen, Chi-Man Pun +1
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camou…
Explicit Visual Prompting for Low-Level Structure Segmentations
Weihuang Liu, Xi Shen, Chi-Man Pun +1
We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow reg…
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
Jianrong Zhang, Yangsong Zhang, Xiaodong Cun +5
In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transfor…
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
Wenxuan Zhang, Xiaodong Cun, Xuan Wang +5
Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modi…
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
Yangtao Wang, Xi Shen, Yuan Yuan +5
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With…
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
Yingyi Chen, Xi Shen, Yahui Liu +2
The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence of this convolution-free network. The fact that ViT works on im…
Compressing Features for Learning with Noisy Labels
Yingyi Chen, Shell Xu Hu, Xi Shen +2
Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the d…