32 citations · 139 across the 21 of their papers we have counts for
20 papers · 1 filter
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Yanjing Li, Sheng Xu, Xianbin Cao +4
Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by…
NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition
Zichang Tan, Jun Li, Jinhao Du +3
Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process sh…
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu, Yanjing Li, Mingbao Lin +4
The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quanti…
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Yanjing Li, Sheng Xu, Baochang Zhang +3
The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems…
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
Weixia Zhang, Dingquan Li, Xiongkuo Min +4
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA mode…
Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu, Yanjing Li, Tiancheng Wang +6
Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an esse…
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention
Xiangcheng Liu, Tianyi Wu, Guodong Guo
Vision transformer has emerged as a new paradigm in computer vision, showing excellent performance while accompanied by expensive computational cost. Image token pruning is one of…
Anti-Retroactive Interference for Lifelong Learning
Runqi Wang, Yuxiang Bao, Baochang Zhang +3
Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive scie…
SKFlow: Learning Optical Flow with Super Kernels
Shangkun Sun, Yuanqi Chen, Yu Zhu +2
Optical flow estimation is a classical yet challenging task in computer vision. One of the essential factors in accurately predicting optical flow is to alleviate occlusions betwee…
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation
Jianrong Zhang, Tianyi Wu, Chuanghao Ding +2
Current semi-supervised semantic segmentation methods mainly focus on designing pixel-level consistency and contrastive regularization. However, pixel-level regularization is sensi…
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
Shan Zhang, Tianyi Wu, Sitong Wu +1
Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query…
Nested Collaborative Learning for Long-Tailed Visual Recognition
Jun Li, Zichang Tan, Jun Wan +2
The networks trained on the long-tailed dataset vary remarkably, despite the same training settings, which shows the great uncertainty in long-tailed learning. To alleviate the unc…
Feature Selective Transformer for Semantic Image Segmentation
Fangjian Lin, Tianyi Wu, Sitong Wu +2
Recently, it has attracted more and more attentions to fuse multi-scale features for semantic image segmentation. Various works were proposed to employ progressive local or global…
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models
Ge Kan, Jinhu Lü, Tian Wang +5
Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process b…
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention
Kai Liu, Tianyi Wu, Cong Liu +1
Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by each query attending to all keys/values, v…
POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
Sheng Xu, Yanjing Li, Junhe Zhao +2
Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address…
Sparse to Dense Motion Transfer for Face Image Animation
Ruiqi Zhao, Tianyi Wu, Guodong Guo
Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a s…
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation
Haitao Lin, Zichang Liu, Chilam Cheang +3
Given a single scene image, this paper proposes a method of Category-level 6D Object Pose and Size Estimation (COPSE) from the point cloud of the target object, without external re…
Fully Transformer Networks for Semantic Image Segmentation
Sitong Wu, Tianyi Wu, Fangjian Lin +2
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent p…
Image-to-Video Generation via 3D Facial Dynamics
Xiaoguang Tu, Yingtian Zou, Jian Zhao +8
We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackl…