168 citations · 628 across the 31 of their papers we have counts for
12 papers · 1 filter
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Xue Li, Tengfei Liang, Yi Jin +2
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning. Existing methods attempt to solve this problem by clust…
Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer
Tao Wang, Hong Liu, Pinhao Song +2
Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing…
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
Xinyu Wang, Min Gui, Yong Jiang +6
Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obta…
Direct Multi-view Multi-person 3D Pose Estimation
Tao Wang, Jianfeng Zhang, Yujun Cai +2
We present Multi-view Pose transformer (MvP) for estimating multi-person 3D poses from multi-view images. Instead of estimating 3D joint locations from costly volumetric representa…
PnP-DETR: Towards Efficient Visual Analysis with Transformers
Tao Wang, Li Yuan, Yunpeng Chen +2
Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translatin…
Learning Class-level Prototypes for Few-shot Learning
Minglei Yuan, Wenhai Wang, Tao Wang +3
Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing met…
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables
Tao Wang, Yong Li, Jingyang Peng +4
Recently, deep learning-based image enhancement algorithms achieved state-of-the-art (SOTA) performance on several publicly available datasets. However, most existing methods fail…
Adaptive Feature Alignment for Adversarial Training
Tao Wang, Ruixin Zhang, Xingyu Chen +6
Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. Many advers…
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Cheng-Ming Chiang, Hsien-Kai Kuo +38
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual…
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Zexin Lu +5
Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network mo…
A Universal Model for Cross Modality Mapping by Relational Reasoning
Zun Li, Congyan Lang, Liqian Liang +4
With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community. Existing method…
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Yongqiang Huang +5
Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and…