73 citations · 261 across the 10 of their papers we have counts for
17 papers
Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness
Manyu Zhu, Dongliang He, Xin Li +5
Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have been w…
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
Fan Yang, Xiao Liu, Dongliang He +5
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into mul…
Image Inpainting with Learnable Bidirectional Attention Maps
Chaohao Xie, Shaohui Liu, Chao Li +5
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular ho…
Deep Concept-wise Temporal Convolutional Networks for Action Localization
Xin Li, Tianwei Lin, Xiao Liu +7
Existing action localization approaches adopt shallow temporal convolutional networks (\ie, TCN) on 1D feature map extracted from video frames. In this paper, we empirically find t…
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Tianwei Lin, Xiao Liu, Xin Li +2
Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-…
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
Chao Li, Dongliang He, Xiao Liu +2
Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. However, there has been li…