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20142023
most citedA Recurrent Encoder-Decoder Network for Sequential Face Alignment

33 citations · 66 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CV20231 cited

Boosting Weakly-Supervised Temporal Action Localization with Text Information

Guozhang Li, De Cheng, Xinpeng Ding +3

Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In…

cs.CV20223 cited

Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification

Zhikang Wang, Yue Bi, Tong Pan +6

Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology. A fundamental challenge of MIL on WSI classification is to d…

cs.CV20223 cited

ALBench: A Framework for Evaluating Active Learning in Object Detection

Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe +5

Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architect…

cs.CV201633 cited

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

Xi Peng, Rogerio S. Feris, Xiaoyu Wang +1

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression lo…

cs.CV20148 cited

Object-centric Sampling for Fine-grained Image Classification

Xiaoyu Wang, Tianbao Yang, Guobin Chen +1

This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-…

cs.CV201412 cited

Generic Object Detection With Dense Neural Patterns and Regionlets

Will Y. Zou, Xiaoyu Wang, Miao Sun +1

This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient gener…