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LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, Spotlight Presentation, Salt Lake City, Utah Abstract FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow
Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset, which is 10 times larger than exis
News 2022-06-01 We release the DeepFashion-MultiModal dataset with rich multi-modal annotations, including manually annotated human parsing labels, manually annotated human keypoints, manually annotated fine-grained labels and textual descriptions. 2020-05-04 Parsing mask annotations and dense pose annotations have been added to “In-shop Clothes Retrieval Benchmark”. Fine-grained attribute annotat
News 2021-09-10 Another related dataset, CelebA-Dialog has been released. 2020-07-10 Two related datasets, CelebAMask-HQ and CelebA-Spoof, have been released. 2016-07-29 If Dropbox is not accessible, please download the dataset using Google Drive or Baidu Drive (password: rp0s). Details CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity im
Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang Department of Informaiton Engineering, The Chinese University of Hong Kong Microsoft Research Abstract We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represente
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