15 citations · 52 across the 13 of their papers we have counts for
13 papers
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding
Shentong Mo, Daizong Liu, Wei Hu
Query-based video grounding is an important yet challenging task in video understanding, which aims to localize the target segment in an untrimmed video according to a sentence que…
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding
Daizong Liu, Xiang Fang, Wei Hu +1
Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query. Most previous works focus on learning frame-le…
Unsupervised Temporal Video Grounding with Deep Semantic Clustering
Daizong Liu, Xiaoye Qu, Yinzhen Wang +5
Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task…
Exploring Motion and Appearance Information for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Pan Zhou +1
This paper addresses temporal sentence grounding. Previous works typically solve this task by learning frame-level video features and align them with the textual information. A maj…
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Xing Di +3
Temporal sentence grounding (TSG) is crucial and fundamental for video understanding. Although the existing methods train well-designed deep networks with a large amount of data, w…
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Pan Zhou
A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence…