577 citations · 2.7k across the 94 of their papers we have counts for
15 papers · 1 filter
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
Bo Li, Wei Wu, Qiang Wang +3
Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region. However, Siamese trackers still have accu…
IRLAS: Inverse Reinforcement Learning for Architecture Search
Minghao Guo, Zhao Zhong, Wei Wu +2
In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topological…
Grid R-CNN
Xin Lu, Buyu Li, Yuxin Yue +2
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditi…
Synaptic Strength For Convolutional Neural Network
Chen Lin, Zhao Zhong, Wei Wu +1
Convolutional Neural Networks(CNNs) are both computation and memory intensive which hindered their deployment in mobile devices. Inspired by the relevant concept in neural science…
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Yongcheng Liu, Lu Sheng, Jing Shao +3
Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs)…
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Xiaohang Zhan, Ziwei Liu, Junjie Yan +2
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes mo…
Localization Guided Learning for Pedestrian Attribute Recognition
Pengze Liu, Xihui Liu, Junjie Yan +1
Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods tr…
Distractor-aware Siamese Networks for Visual Object Tracking
Zheng Zhu, Qiang Wang, Bo Li +3
Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking app…
BlockQNN: Efficient Block-wise Neural Network Architecture Generation
Zhao Zhong, Zichen Yang, Boyang Deng +4
Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and ela…
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
Guanglu Song, Yu Liu, Ming Jiang +3
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared…
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Yi Wei, Xinyu Pan, Hongwei Qin +2
In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very ti…
Exploring Disentangled Feature Representation Beyond Face Identification
Yu Liu, Fangyin Wei, Jing Shao +3
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification. Specifically, we construct an identity Distilling and Dis…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo +2
Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data…
FOTS: Fast Oriented Text Spotting with a Unified Network
Xuebo Liu, Ding Liang, Shi Yan +3
Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and re…
Accelerated Training for Massive Classification via Dynamic Class Selection
Xingcheng Zhang, Lei Yang, Junjie Yan +1
Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world system…