676 citations · 4.7k across the 170 of their papers we have counts for
19 papers · 1 filter
Context-Aware Semantic Inpainting
Haofeng Li, Guanbin Li, Liang Lin +1
Recently image inpainting has witnessed rapid progress due to generative adversarial networks (GAN) that are able to synthesize realistic contents. However, most existing GAN-based…
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
Tianshui Chen, Liang Lin, Wangmeng Zuo +2
Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computi…
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
Tianshui Chen, Zhouxia Wang, Guanbin Li +1
Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions…
LSTM Pose Machines
Yue Luo, Jimmy Ren, Zhouxia Wang +5
We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior…
Multi-label Image Recognition by Recurrently Discovering Attentional Regions
Zhouxia Wang, Tianshui Chen, Guanbin Li +2
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for…
Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning
Dongyu Zhang, Liang Lin, Tianshui Chen +3
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, sev…
Learning to Segment Human by Watching YouTube
Xiaodan Liang, Yunchao Wei, Liang Lin +4
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 informatio…
Visual Tracking via Dynamic Graph Learning
Chenglong Li, Liang Lin, Wangmeng Zuo +2
Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by…
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Qingxing Cao, Liang Lin, Yukai Shi +2
Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing…
Recurrent 3D Pose Sequence Machines
Mude Lin, Liang Lin, Xiaodan Liang +2
3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherent…
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Ziliang Chen, Keze Wang, Xiao Wang +3
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS)…
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Yukai Shi, Keze Wang, Chongyu Chen +2
Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due t…
Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction
Liang Lin, Lili Huang, Tianshui Chen +2
This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in co…
Instance-Level Salient Object Segmentation
Guanbin Li, Yuan Xie, Liang Lin +1
Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances…
Interpretable Structure-Evolving LSTM
Xiaodan Liang, Liang Lin, Xiaohui Shen +3
This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures.…
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Ke Gong, Xiaodan Liang, Dongyu Zhang +2
Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, an…
Progressively Diffused Networks for Semantic Image Segmentation
Ruimao Zhang, Wei Yang, Zhanglin Peng +2
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemp…
Cost-Effective Active Learning for Deep Image Classification
Keze Wang, Dongyu Zhang, Ya Li +2
Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In t…
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification
Liang Lin, Keze Wang, Deyu Meng +2
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of differe…