Shengye Yan

Nanjing University of Information Science and Technology

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Xilin Chen

Institute of Computing Technology

Shiguang Shan

Chinese Academy of Sciences

Wen Gao

Peking University

Jie Chen

China Electronics Technology Group Corporation

Wen Gao

Peking University

Xinxing Xu

Ocean University of China

Qingshan Liu

Tianjin Medical University

Ruiping Wang

Beijing Jiaotong University

Dong Xu

University of Sydney

Stephen Lin

Chung Shan Medical University Hospital

Cooperative Institutions

Nanjing University of Information Science and Technology

Chinese Academy of Sciences

Harbin Institute of Technology

Institute of Computing Technology

Peng Cheng Laboratory

University of Chinese Academy of Sciences

China Meteorological Administration

Peking University

Beijing Institute of Technology

Shanghai Electric (China)

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Context-BMN for Temporal Action Proposal Generation

Lecture notes in computer science (2021)

Tang Bao-qing Shengye Yan Yihua Ni Yongjia Yang Kang Pan

10.1007/978-3-030-93046-2_51

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Citations (2)

Rotated face detection in color images using radial template (RT)

Heng Liu Shengye Yan Xilin Chen Wen Gao

In this paper, we propose a face detection algorithm to locate faces rotated in any orientation. Detecting rotated faces is important for a face detection system. First, we present a novel model named radial template (RT) to detect rotated faces. This template is designed to find stable features of center-rotated objects in edge maps. Based on skin detection and edge extraction, our method searches for face-like areas and gets their orientations by RT searching. Then the candidates are rotated upright and a frontal face detector is used to determine existence of faces. A system integrating these techniques is presented. Experimental results show that our algorithm is effective to detect human faces rotated in any angle with different sizes, lighting conditions and backgrounds.

Object-class detection

10.1109/icme.2003.1221267

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Citations (5)

Rotated face detection in color images using radial template (RT)

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). (2003)

Heng Liu Shengye Yan Xilin Chen Wen Gao

We propose a face detection algorithm to locate faces rotated in any orientation. Detecting rotated faces is important for a face detection system. First, we present a novel model named radial template (RT) to detect rotated faces. This template is designed to find stable features of center-rotated objects in edge maps. Based on skin detection and edge extraction, our method searches for face-like areas and gets their orientations by RT searching. Then the candidates are rotated upright and a frontal face detector is used to determine the existence of faces. A system integrating these techniques is presented. Experimental results show that our algorithm is effective for detecting human faces rotated at any angle with different sizes, lighting conditions and backgrounds.

Object-class detection

10.1109/icassp.2003.1199145

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Citations (0)

Enhancing Human Face Detection by Resampling Examples Through Manifolds

IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans (2007)

Jie Chen Ruiping Wang Shengye Yan Shiguang Shan Xilin Chen

As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face detector is a quite challenging problem. In this paper, we propose a method to resample a representative training set from a collected large-scale database to train a robust human face detector. First, in a high-dimensional space, we estimate geodesic distances between pairs of face samples/examples inside the collected face set by isometric feature mapping (Isomap) and then subsample the face set. After that, we embed the face set to a low-dimensional manifold space and obtain the low-dimensional embedding. Subsequently, in the embedding, we interweave the face set based on the weights computed by locally linear embedding (LLE). Furthermore, we resample nonfaces by Isomap and LLE likewise. Using the resulting face and nonface samples, we train an AdaBoost-based face detector and run it on a large database to collect false alarms. We then use the false detections to train a one-class support vector machine (SVM). Combining the AdaBoost and one-class SVM-based face detector, we obtain a stronger detector. The experimental results on the MIT + CMU frontal face test set demonstrated that the proposed method significantly outperforms the other state-of-the-art methods.

Isomap

AdaBoost

Object-class detection

10.1109/tsmca.2007.906575

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Citations (41)

Sparse multiple instance learning as document classification

Multimedia Tools and Applications (2016)

Shengye Yan Xiaodong Zhu Guoqing Liu Jianxin Wu

Document classification

Representation

Similarity (geometry)

Multi-label classification

10.1007/s11042-016-3567-z

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Citations (8)

2D Cascaded AdaBoost for Eye Localization

2022 26th International Conference on Pattern Recognition (ICPR) (2006)

Zhiheng Niu Shiguang Shan Shengye Yan Xilin Chen Wen Gao

In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term "2D", we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples (please refer to Fig.1). The advantages of the 2D structure include: (1) It greatly facilitates the classifier designing on huge-scale training set; (2) It can easily deal with the significant variations within the positive (or negative) samples; (3) Both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method.

AdaBoost

10.1109/icpr.2006.1194

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Citations (125)

Region-Based Spatial Sampling for Image Classification

Pengpeng Ji Shengye Yan Qingshan Liu

Local descriptors with Bag-of-Words representation were widely used for image classification. Especially, local descriptors of dense spatial sampling were demonstrated to be able to further improve performances of image classification. However, denser spatial sampling is impractical due to huge computation cost. To handle this issue, we propose a new region-based sampling strategy in this paper. We first perform an over-segmentation to get image regions, and then we extract local descriptors around the region boundaries and inside the regions respectively with a popular sampling strategy. Thus, almost at the same computation cost, the proposed method can capture more salient points and obtain much better classification performance. Extensive experiments are conducted on two widely-used datasets (UIUC Sports and Caltech-101). The experimental results demonstrate the effectiveness of the proposed method. Specifically, the proposed method updates the state-of-the-art on UIUC Sports dataset with a classification accuracy of 89.38%.

Contextual image classification

Representation

10.1109/icig.2013.177

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Citations (1)

Internal Location Assistance for Temporal Action Proposal Generation

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2024)

Songsong Feng Shengye Yan

Temporal action proposal generation (TAPG) aims to locate action instances in untrimmed videos for video analysis tasks. In this paper, we propose a novel approach called Internal Location Assistance Net (ILAN) to take advantage of the internal action point instead of only utilizing the start and end points themselves. Specifically, instead of solely predicting the action's start and end positions, we also predict extra internal points which could be the one-eighth, one-fourth or center points etc. Then the pair of left and right internal positions of the action are matched to generate center region proposals. Then the predicted center region is constrained to lie in the predicted overall action region. And both confidences of the overall action region and the center region are combined to get the final action proposal. Besides, we incorporate the window transformer to enhance the feature extraction for capturing more precise action boundaries. Extensive experiments are conducted on two popular benchmark datasets: THUMOS14 and ActivityNet-v1.3. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts.

10.1109/icassp48485.2024.10447851

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Citations (1)

Learning the object location, scale and view for image categorization with adapted classifier

Information Sciences (2014)

Shengye Yan Xinxing Xu Qingshan Liu

Multiple kernel learning

Adaptive Learning

Contextual image classification

Concept learning

10.1016/j.ins.2014.03.041

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Citations (4)

Image Classification With Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning

IEEE Transactions on Cybernetics (2014)

Shengye Yan Xinxing Xu Dong Xu Stephen Lin Xuelong Li

We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings.

Multiple kernel learning

Kernel (algebra)

Benchmark (surveying)

Abstraction

10.1109/tcyb.2014.2326596

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Citations (35)