676 citations · 4.7k across the 170 of their papers we have counts for
19 papers · 2 filters
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
Jihan Yang, Ruijia Xu, Ruiyu Li +4
We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution o…
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
Changlin Li, Jiefeng Peng, Liuchun Yuan +4
Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is hoped and expected to bring about a new revolution in machine learning. De…
Knowledge Graph Transfer Network for Few-Shot Recognition
Riquan Chen, Tianshui Chen, Xiaolu Hui +3
Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel cat…
Layout-Graph Reasoning for Fashion Landmark Detection
Weijiang Yu, Xiaodan Liang, Ke Gong +3
Detecting dense landmarks for diverse clothes, as a fundamental technique for clothes analysis, has attracted increasing research attention due to its huge application potential. H…
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network
Qingxing Cao, Bailin Li, Xiaodan Liang +1
Explanation and high-order reasoning capabilities are crucial for real-world visual question answering with diverse levels of inference complexity (e.g., what is the dog that is ne…
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Xiaopeng Yan, Ziliang Chen, Anni Xu +3
Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived fro…
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
Zhanghui Kuang, Yiming Gao, Guanbin Li +4
Matching clothing images from customers and online shopping stores has rich applications in E-commerce. Existing algorithms encoded an image as a global feature vector and performe…
Crowd Counting with Deep Structured Scale Integration Network
Lingbo Liu, Zhilin Qiu, Guanbin Li +3
Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people. In this p…
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Tianshui Chen, Muxin Xu, Xiaolu Hui +2
Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency.…
Learning Compact Target-Oriented Feature Representations for Visual Tracking
Chenglong Li, Yan Huang, Liang Wang +2
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundan…
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Pengxiang Yan, Guanbin Li, Yuan Xie +4
Deep learning-based video salient object detection has recently achieved great success with its performance significantly outperforming any other unsupervised methods. However, exi…
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning
Yukai Shi, Guanbin Li, Qingxing Cao +2
Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input. In contrast to the existi…
Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition
Guanbin Li, Xin Zhu, Yirui Zeng +2
Facial action unit (AU) recognition is a crucial task for facial expressions analysis and has attracted extensive attention in the field of artificial intelligence and computer vis…
Graphonomy: Universal Human Parsing via Graph Transfer Learning
Ke Gong, Yiming Gao, Xiaodan Liang +3
Prior highly-tuned human parsing models tend to fit towards each dataset in a specific domain or with discrepant label granularity, and can hardly be adapted to other human parsing…
Adaptively Connected Neural Networks
Guangrun Wang, Keze Wang, Liang Lin
This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) {in} two aspects. First, ACNet employs a fle…
Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
Guangrun Wang, Guangcong Wang, Xujie Zhang +3
Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 [1] and Market-1501 [2]), which are quite expensive because each…
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
Xipeng Chen, Kwan-Yee Lin, Wentao Liu +3
Recent studies have shown remarkable advances in 3D human pose estimation from monocular images, with the help of large-scale in-door 3D datasets and sophisticated network architec…
Knowledge-Embedded Routing Network for Scene Graph Generation
Tianshui Chen, Weihao Yu, Riquan Chen +1
To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since t…
3D Human Pose Machines with Self-supervised Learning
Keze Wang, Liang Lin, Chenhan Jiang +2
Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this tas…