89 citations · 140 across the 9 of their papers we have counts for
9 papers
You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos
Xiang Fang, Daizong Liu, Pan Zhou +1
Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made…
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
Guoshun Nan, Guoqing Luo, Sicong Leng +2
Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues. Existing relation extraction models may be unsatisfa…
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition
Shuyun Tang, Zhaojie Luo, Guoshun Nan +2
Automatic emotion recognition (AER) based on enriched multimodal inputs, including text, speech, and visual clues, is crucial in the development of emotionally intelligent machines…
Uncovering Main Causalities for Long-tailed Information Extraction
Guoshun Nan, Jiaqi Zeng, Rui Qiao +2
Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset, may le…
Interventional Video Grounding with Dual Contrastive Learning
Guoshun Nan, Rui Qiao, Yao Xiao +4
Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with var…
Video Corpus Moment Retrieval with Contrastive Learning
Hao Zhang, Aixin Sun, Wei Jing +4
Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corres…
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Xu Wang, Shuai Zhao, Bo Cheng +5
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most o…
Modeling Topical Relevance for Multi-Turn Dialogue Generation
Hainan Zhang, Yanyan Lan, Liang Pang +3
Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect th…
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Guoshun Nan, Zhijiang Guo, Ivan Sekulić +1
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence ent…