140 citations · 599 across the 19 of their papers we have counts for
19 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…
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Ming Liu, Yukang Ding, Min Xia +4
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
Dongliang He, Xiang Zhao, Jizhou Huang +3
The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted st…
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
Dongliang He, Zhichao Zhou, Chuang Gan +5
Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for the spatial-temporal modeling in video…
Fine-grained Video Categorization with Redundancy Reduction Attention
Chen Zhu, Xiao Tan, Feng Zhou +4
For fine-grained categorization tasks, videos could serve as a better source than static images as videos have a higher chance of containing discriminative patterns. Nevertheless,…
Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories
Yaming Wang, Xiao Tan, Yi Yang +4
Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new large-scale dataset that c…
3D Pose Estimation for Fine-Grained Object Categories
Yaming Wang, Xiao Tan, Yi Yang +4
Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new lar…
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Xiang Long, Chuang Gan, Gerard de Melo +3
Recently, substantial research effort has focused on how to apply CNNs or RNNs to better extract temporal patterns from videos, so as to improve the accuracy of video classificatio…
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Yunlong Bian, Chuang Gan, Xiao Liu +7
This paper describes our solution for the video recognition task of ActivityNet Kinetics challenge that ranked the 1st place. Most of existing state-of-the-art video recognition ap…
Deep Metric Learning with Angular Loss
Jian Wang, Feng Zhou, Shilei Wen +2
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images…
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
Fu Li, Chuang Gan, Xiao Liu +6
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place. Because the challenge p…
Dynamic Computational Time for Visual Attention
Zhichao Li, Yi Yang, Xiao Liu +3
We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM). Rather than attention with a fixed number of steps for…
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
Xiao Liu, Jiang Wang, Shilei Wen +2
A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localiz…
Fully Convolutional Attention Networks for Fine-Grained Recognition
Xiao Liu, Tian Xia, Jiang Wang +3
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to local…