15 citations · 102 across the 35 of their papers we have counts for
11 papers · 1 filter
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Xiaoye Qu, Jun Zeng, Daizong Liu +3
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples. Unfortunately, the dist…
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval
Xiang Fang, Daizong Liu, Pan Zhou +1
As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given la…
Hierarchical Local-Global Transformer for Temporal Sentence Grounding
Xiang Fang, Daizong Liu, Pan Zhou +2
This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a g…
Reducing the Vision and Language Bias for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Wei Hu
Temporal sentence grounding (TSG) is an important yet challenging task in multimedia information retrieval. Although previous TSG methods have achieved decent performance, they ten…
Skimming, Locating, then Perusing: A Human-Like Framework for Natural Language Video Localization
Daizong Liu, Wei Hu
This paper addresses the problem of natural language video localization (NLVL). Almost all existing works follow the "only look once" framework that exploits a single model to dire…
Gaussian Kernel-based Cross Modal Network for Spatio-Temporal Video Grounding
Zeyu Xiong, Daizong Liu, Pan Zhou
Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to localize the spatio-temporal tube of the interested object semantically according to a natural language…
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…