13 citations · 27 across the 12 of their papers we have counts for
7 papers · 1 filter
FAT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
Yaguan Qian, Chenyu Zhao, Zhaoquan Gu +5
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as se…
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Jianfeng Dong +6
This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-quer…
Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos
Daizong Liu, Pan Zhou
Temporal sentence localization in videos (TSLV) aims to retrieve the most interested segment in an untrimmed video according to a given sentence query. However, almost of existing…
Tracking Objects and Activities with Attention for Temporal Sentence Grounding
Zeyu Xiong, Daizong Liu, Pan Zhou +1
Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video.Most existing methods extr…
Hypotheses Tree Building for One-Shot Temporal Sentence Localization
Daizong Liu, Xiang Fang, Pan Zhou +3
Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent ac…
Video Graph Transformer for Video Question Answering
Junbin Xiao, Pan Zhou, Tat-Seng Chua +1
This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module whi…