676 citations · 4.7k across the 170 of their papers we have counts for
36 papers · 1 filter
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Sirui Xie, Junning Huang, Lanxin Lei +4
Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent ex…
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
Lingbo Liu, Guanbin Li, Yuan Xie +3
Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network…
SNAS: Stochastic Neural Architecture Search
Sirui Xie, Hehui Zheng, Chunxiao Liu +1
We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architect…
FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking
Yabin Zhu, Chenglong Li, Bin Luo +1
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose…
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Ruijia Xu, Guanbin Li, Jihan Yang +1
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the…
Cross-Modal Attentional Context Learning for RGB-D Object Detection
Guanbin Li, Yukang Gan, Hejun Wu +2
Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications such as robot graspin…
Hybrid Knowledge Routed Modules for Large-scale Object Detection
Chenhan Jiang, Hang Xu, Xiangdan Liang +1
The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene. This paradigm lead…
Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview
Lili Huang, Jiefeng Peng, Ruimao Zhang +2
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused wid…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Yuan Yuan, Siyuan Liu, Jiawei Zhang +3
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from tradi…
Interpretable Visual Question Answering by Reasoning on Dependency Trees
Qingxing Cao, Bailin Li, Xiaodan Liang +1
Collaborative reasoning for understanding image-question pairs is a very critical but underexplored topic in interpretable visual question answering systems. Although very recent s…
Attentive Crowd Flow Machines
Lingbo Liu, Ruimao Zhang, Jiefeng Peng +3
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow ch…
Neural Task Planning with And-Or Graph Representations
Tianshui Chen, Riquan Chen, Lin Nie +3
This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer…
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Tianshui Chen, Wenxi Wu, Yuefang Gao +3
Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized acc…
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Qixian Zhou, Xiaodan Liang, Ke Gong +1
Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level…
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Guanbin Li, Xiang He, Wei Zhang +3
Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based me…
Instance-level Human Parsing via Part Grouping Network
Ke Gong, Xiaodan Liang, Yicheng Li +3
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing…
Crowd Counting using Deep Recurrent Spatial-Aware Network
Lingbo Liu, Hongjun Wang, Guanbin Li +2
Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera'…
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition…
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
Zhouxia Wang, Tianshui Chen, Jimmy Ren +3
Social relationships (e.g., friends, couple etc.) form the basis of the social network in our daily life. Automatically interpreting such relationships bears a great potential for…
Toward Characteristic-Preserving Image-based Virtual Try-On Network
Bochao Wang, Huabin Zheng, Xiaodan Liang +3
Image-based virtual try-on systems for fitting new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. A desirable pipeline…
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
Ruimao Zhang, Hongbin Sun, Jingyu Li +4
Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually imp…
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
Keze Wang, Liang Lin, Xiaopeng Yan +3
Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due t…
DRPose3D: Depth Ranking in 3D Human Pose Estimation
Min Wang, Xipeng Chen, Wentao Liu +3
In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking c…