23 citations · 81 across the 19 of their papers we have counts for
7 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…
Backdoor Attacks on Crowd Counting
Yuhua Sun, Tailai Zhang, Xingjun Ma +6
Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveil…
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…
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…