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20152026
most citedFeature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

175 citations · 634 across the 52 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.CV2017

Towards Effective Low-bitwidth Convolutional Neural Networks

Bohan Zhuang, Chunhua Shen, Mingkui Tan +2

This paper tackles the problem of training a deep convolutional neural network with both low-precision weights and low-bitwidth activations. Optimizing a low-precision network is v…

cs.CV2017

Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization

Yu Chen, Chunhua Shen, Hao Chen +3

Landmark/pose estimation in single monocular images have received much effort in computer vision due to its important applications. It remains a challenging task when input images…

cs.CV2017★ 15 cited

Visually Aligned Word Embeddings for Improving Zero-shot Learning

Ruizhi Qiao, Lingqiao Liu, Chunhua Shen +1

Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes. Recently, distributed word embeddings (DWE) pre-trained from large text…

cs.CV2017

Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation

Yu Chen, Chunhua Shen, Xiu-Shen Wei +2

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically…

cs.CV2017

Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation

Tong Shen, Guosheng Lin, Lingqiao Liu +2

Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour an…

cs.CV2017

Towards Context-aware Interaction Recognition

Bohan Zhuang, Lingqiao Liu, Chunhua Shen +1

Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current…