no code implementations • CVPR 2022 • Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, Jianchao Yang
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
1 code implementation • ICCV 2021 • Yuxi Ren, Jie Wu, Xuefeng Xiao, Jianchao Yang
It reveals that OMGD provides a feasible solution for the deployment of real-time image translation on resource-constrained devices.
no code implementations • 27 Apr 2021 • Chaosheng Dong, Xiaojie Jin, Weihao Gao, Yijia Wang, Hongyi Zhang, Xiang Wu, Jianchao Yang, Xiaobing Liu
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
no code implementations • 7 Apr 2020 • Jian Ren, Menglei Chai, Sergey Tulyakov, Chen Fang, Xiaohui Shen, Jianchao Yang
In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video.
1 code implementation • ICLR 2020 • Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, Jianchao Yang
We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that is much smaller than the ones used in recent NAS algorithms.
Ranked #61 on Neural Architecture Search on ImageNet
no code implementations • ICLR 2020 • Daquan Zhou, Xiaojie Jin, Qibin Hou, Kaixin Wang, Jianchao Yang, Jiashi Feng
The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs).
8 code implementations • 17 Jun 2019 • Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
4 code implementations • ICLR 2019 • Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang
Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization.
no code implementations • 6 Sep 2018 • Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, Thomas Huang
End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
4 code implementations • ECCV 2018 • Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
Ranked #12 on Video Object Segmentation on YouTube-VOS 2018 (F-Measure (Unseen) metric)
12 code implementations • 27 Aug 2018 • Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, Thomas Huang
Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
Ranked #4 on Multi-Frame Super-Resolution on PROBA-V
no code implementations • CVPR 2019 • Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran
In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain.
no code implementations • 4 Jun 2018 • Jian Ren, Jianchao Yang, Ning Xu, David J. Foran
In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks.
1 code implementation • CVPR 2018 • Linjie Yang, Yanran Wang, Xuehan Xiong, Jianchao Yang, Aggelos K. Katsaggelos
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
Ranked #1 on One-shot visual object segmentation on YouTube-VOS 2018 (Jaccard (Seen) metric)
no code implementations • 31 Jan 2018 • Zhengyuan Yang, Yuncheng Li, Jianchao Yang, Jiebo Luo
The attention mechanism is important for skeleton based action recognition because there exist spatio-temporal key stages while the joint predictions can be inaccurate.
no code implementations • 5 Jan 2018 • Yingzhen Yang, Jianchao Yang, Ning Xu, Wei Han
Due to the weight sharing scheme, the parameter size of the $3$D-FilterMap is much smaller than that of the filters to be learned in the conventional convolution layer when $3$D-FilterMap generates the same number of filters.
no code implementations • ICLR 2018 • Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Jiashi Feng, Shuicheng Yan
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.
no code implementations • ICML 2018 • Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.
no code implementations • 4 Oct 2017 • Xiaodan Liang, Yunchao Wei, Liang Lin, Yunpeng Chen, Xiaohui Shen, Jianchao Yang, Shuicheng Yan
An intuition on human segmentation is that when a human is moving in a video, the video-context (e. g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body.
no code implementations • 10 Sep 2017 • Bowen Cheng, Zhangyang Wang, Zhaobin Zhang, Zhu Li, Ding Liu, Jianchao Yang, Shuai Huang, Thomas S. Huang
Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.
no code implementations • 5 Sep 2017 • Yingzhen Yang, Jiashi Feng, Nebojsa Jojic, Jianchao Yang, Thomas S. Huang
We study the proximal gradient descent (PGD) method for $\ell^{0}$ sparse approximation problem as well as its accelerated optimization with randomized algorithms in this paper.
no code implementations • 12 Mar 2017 • Yang Zhao, Ronggang Wang, Wei Jia, Jianchao Yang, Wenmin Wang, Wen Gao
The proposed method consists of a learning stage and a reconstructing stage.
no code implementations • ICCV 2017 • Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain.
1 code implementation • CVPR 2017 • Linjie Yang, Kevin Tang, Jianchao Yang, Li-Jia Li
The goal is to densely detect visual concepts (e. g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase.
1 code implementation • journals 2016 • Ding Liu, Zhaowen Wang, Bihan Wen, Student Member, Jianchao Yang, Member, Wei Han, and Thomas S. Huang, Fellow, IEEE
We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.
2 code implementations • 9 May 2016 • Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
We hope that this data set encourages further research on visual emotion analysis.
no code implementations • 29 Apr 2016 • Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo, Shuicheng Yan
To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.
no code implementations • ICCV 2015 • Xiaodan Liang, Chunyan Xu, Xiaohui Shen, Jianchao Yang, Si Liu, Jinhui Tang, Liang Lin, Shuicheng Yan
In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network.
no code implementations • 28 Oct 2015 • Yingzhen Yang, Jiashi Feng, Jianchao Yang, Thomas S. Huang
Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC) \cite{ElhamifarV13} and $\ell^{1}$-graph \cite{YanW09, ChengYYFH10}, are effective in partitioning the data that lie in a union of subspaces.
no code implementations • 20 Sep 2015 • Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis.
no code implementations • 9 Sep 2015 • Xiaodan Liang, Yunchao Wei, Xiaohui Shen, Jianchao Yang, Liang Lin, Shuicheng Yan
Instance-level object segmentation is an important yet under-explored task.
no code implementations • ICCV 2015 • Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, Thomas Huang
We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end.
1 code implementation • 12 Jul 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers.
Ranked #1 on Font Recognition on VFR-Wild
no code implementations • CVPR 2015 • Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories.
no code implementations • CVPR 2015 • Haichao Zhang, Jianchao Yang
The proposed method effectively leverages the information distributed across multiple video frames due to camera motion, jointly estimating the motion between consecutive frames and blur within each frame.
no code implementations • 22 Apr 2015 • Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Wei Han, Jianchao Yang, Thomas S. Huang
Deep learning has been successfully applied to image super resolution (SR).
no code implementations • CVPR 2015 • Si Liu, Xiaodan Liang, Luoqi Liu, Xiaohui Shen, Jianchao Yang, Changsheng Xu, Liang Lin, Xiaochun Cao, Shuicheng Yan
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image.
no code implementations • 31 Mar 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We address a challenging fine-grain classification problem: recognizing a font style from an image of text.
no code implementations • 12 Mar 2015 • Zhangyang Wang, Yingzhen Yang, Jianchao Yang, Thomas S. Huang
We study the complementary behaviors of external and internal examples in image restoration, and are motivated to formulate a composite dictionary design framework.
1 code implementation • 9 Mar 2015 • Xiaodan Liang, Si Liu, Xiaohui Shen, Jianchao Yang, Luoqi Liu, Jian Dong, Liang Lin, Shuicheng Yan
The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters.
no code implementations • 3 Mar 2015 • Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Jianchao Yang, Thomas S. Huang
Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a lowresolution (LR) input.
no code implementations • CVPR 2015 • Chen Fang, Hailin Jin, Jianchao Yang, Zhe Lin
We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities.
no code implementations • 18 Dec 2014 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data.
no code implementations • NeurIPS 2014 • Haichao Zhang, Jianchao Yang
The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure.
no code implementations • CVPR 2014 • Guang Chen, Jianchao Yang, Hailin Jin, Jonathan Brandt, Eli Shechtman, Aseem Agarwala, Tony X. Han
This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content.
Ranked #1 on Font Recognition on VFR-447
no code implementations • CVPR 2014 • Ketan Tang, Jianchao Yang, Jue Wang
Haze is one of the major factors that degrade outdoor images.
no code implementations • CVPR 2014 • Jian Dong, Qiang Chen, Xiaohui Shen, Jianchao Yang, Shuicheng Yan
We study the problem of human body configuration analysis, more specifically, human parsing and human pose estimation.
no code implementations • 24 Apr 2014 • Zhaowen Wang, Jianchao Yang, Zhe Lin, Jonathan Brandt, Shiyu Chang, Thomas Huang
In this paper, we present an image similarity learning method that can scale well in both the number of images and the dimensionality of image descriptors.
no code implementations • 21 Dec 2013 • Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang
The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision.
no code implementations • CVPR 2013 • Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang
Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image.
no code implementations • CVPR 2013 • Jianchao Yang, Zhe Lin, Scott Cohen
Extensive experiments on benchmark and realworld images demonstrate that our algorithm can produce natural-looking results with sharp edges and preserved fine details, while the current state-of-the-art algorithms are prone to visual artifacts.
no code implementations • CVPR 2013 • Haoxiang Li, Gang Hua, Zhe Lin, Jonathan Brandt, Jianchao Yang
By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatialappearance distribution of all face images in the training corpus.