107 citations · 380 across the 45 of their papers we have counts for
10 papers · 1 filter
-Equivariant Vision Transformer
Renjun Xu, Kaifan Yang, Ke Liu +1
Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivar…
Human-imperceptible, Machine-recognizable Images
Fusheng Hao, Fengxiang He, Yikai Wang +4
Massive human-related data is collected to train neural networks for computer vision tasks. A major conflict is exposed relating to software engineers between better developing AI…
Bridged Transformer for Vision and Point Cloud 3D Object Detection
Yikai Wang, TengQi Ye, Lele Cao +4
3D object detection is a crucial research topic in computer vision, which usually uses 3D point clouds as input in conventional setups. Recently, there is a trend of leveraging mul…
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
Minghui Chen, Cheng Wen, Feng Zheng +2
Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been t…
Siamese Network with Interactive Transformer for Video Object Segmentation
Meng Lan, Jing Zhang, Fengxiang He +1
Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied…
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
Yue He, Chen Chen, Jing Zhang +4
Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) m…
Self-Ensembling GAN for Cross-Domain Semantic Segmentation
Yonghao Xu, Fengxiang He, Bo Du +2
Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level…
Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction
Yikai Wang, Fuchun Sun, Wenbing Huang +2
Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same c…
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
Wen Wang, Yang Cao, Jing Zhang +4
Detection transformers have recently shown promising object detection results and attracted increasing attention. However, how to develop effective domain adaptation techniques to…
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Sheng Li, Fengxiang He, Bo Du +3
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, emp…