305 citations · 672 across the 21 of their papers we have counts for
21 papers
Limb-Aware Virtual Try-On Network with Progressive Clothing Warping
Shengping Zhang, Xiaoyu Han, Weigang Zhang +3
Image-based virtual try-on aims to transfer an in-shop clothing image to a person image. Most existing methods adopt a single global deformation to perform clothing warping directl…
End-to-End Human Instance Matting
Qinglin Liu, Shengping Zhang, Quanling Meng +3
Human instance matting aims to estimate an alpha matte for each human instance in an image, which is extremely challenging and has rarely been studied so far. Despite some efforts…
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency
Shuo Yang, Zhaopan Xu, Kai Wang +4
As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, mass…
Learning cross space mapping via DNN using large scale click-through logs
Wei Yu, Kuiyuan Yang, Yalong Bai +2
The gap between low-level visual signals and high-level semantics has been progressively bridged by continuous development of deep neural network (DNN). With recent progress of DNN…
Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
Haosen Yang, Deng Huang, Bin Wen +5
Masked autoencoders (MAEs) have emerged recently as art self-supervised spatiotemporal representation learners. Inheriting from the image counterparts, however, existing video MAEs…
Spatio-Temporal Deformable Attention Network for Video Deblurring
Huicong Zhang, Haozhe Xie, Hongxun Yao
The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames. Therefore, mainst…
Temporal Action Proposal Generation with Background Constraint
Haosen Yang, Wenhao Wu, Lining Wang +4
Temporal action proposal generation (TAPG) is a challenging task that aims to locate action instances in untrimmed videos with temporal boundaries. To evaluate the confidence of pr…
Temporal Action Proposal Generation with Transformers
Lining Wang, Haosen Yang, Wenhao Wu +2
Transformer networks are effective at modeling long-range contextual information and have recently demonstrated exemplary performance in the natural language processing domain. Con…
Efficient Regional Memory Network for Video Object Segmentation
Haozhe Xie, Hongxun Yao, Shangchen Zhou +2
Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for seg…
GRNet: Gridding Residual Network for Dense Point Cloud Completion
Haozhe Xie, Hongxun Yao, Shangchen Zhou +3
Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer P…
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
Haozhe Xie, Hongxun Yao, Shengping Zhang +2
Recovering the 3D shape of an object from single or multiple images with deep neural networks has been attracting increasing attention in the past few years. Mainstream works (e.g.…
Scene Text Recognition via Transformer
Xinjie Feng, Hongxun Yao, Yuankai Qi +2
Scene text recognition with arbitrary shape is very challenging due to large variations in text shapes, fonts, colors, backgrounds, etc. Most state-of-the-art algorithms rectify th…
Adaptive Semantic-Visual Tree for Hierarchical Embeddings
Shuo Yang, Wei Yu, Ying Zheng +2
Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correl…
Single-View 3D Object Reconstruction from Shape Priors in Memory
Shuo Yang, Min Xu, Haozhe Xie +2
Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images contai…
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
Sheng Jin, Shangchen Zhou, Yao Liu +4
Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting suffici…
Toward 3D Object Reconstruction from Stereo Images
Haozhe Xie, Hongxun Yao, Shangchen Zhou +3
Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the train…
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Ying Zheng, Hongxun Yao, Xiaoshuai Sun +3
Sketch recognition remains a significant challenge due to the limited training data and the substantial intra-class variance of freehand sketches for the same object. Conventional…
Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning
Ying Zheng, Hongxun Yao, Xiaoshuai Sun
In this paper, we propose a novel deep framework for part-level semantic parsing of freehand sketches, which makes three main contributions that are experimentally shown to have su…
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
Haozhe Xie, Hongxun Yao, Xiaoshuai Sun +2
Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several ma…
Deep Saliency Hashing
Sheng Jin, Hongxun Yao, Xiaoshuai Sun +3
In recent years, hashing methods have been proved to be effective and efficient for the large-scale Web media search. However, the existing general hashing methods have limited dis…
Visualizing and Comparing Convolutional Neural Networks
Wei Yu, Kuiyuan Yang, Yalong Bai +2
Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task. To achieve better performance, the complexi…