584 citations · 1.9k across the 94 of their papers we have counts for
10 papers · 1 filter
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Yujin Yuan, Liyuan Liu, Siliang Tang +5
Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated train…
A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks
Weijie Chen, Yuan Zhang, Di Xie +1
Neuron pruning is an efficient method to compress the network into a slimmer one for reducing the computational cost and storage overhead. Most of state-of-the-art results are obta…
Learning Incremental Triplet Margin for Person Re-identification
Yingying Zhang, Qiaoyong Zhong, Liang Ma +2
Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric…
Counterfactual Critic Multi-Agent Training for Scene Graph Generation
Long Chen, Hanwang Zhang, Jun Xiao +3
Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understa…
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng +4
This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only superv…
Extreme Network Compression via Filter Group Approximation
Bo Peng, Wenming Tan, Zheyang Li +3
In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs…
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
Tao Song, Leiyu Sun, Di Xie +2
A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should part…
A practical convolutional neural network as loop filter for intra frame
Xiaodan Song, Jiabao Yao, Lulu Zhou +4
Loop filters are used in video coding to remove artifacts or improve performance. Recent advances in deploying convolutional neural network (CNN) to replace traditional loop filter…
Edit Probability for Scene Text Recognition
Fan Bai, Zhanzhan Cheng, Yi Niu +2
We consider the scene text recognition problem under the attention-based encoder-decoder framework, which is the state of the art. The existing methods usually employ a frame-wise…
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Chao Li, Qiaoyong Zhong, Di Xie +1
Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most crucial factors for this task lie…