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20202023
most citedVideo Corpus Moment Retrieval with Contrastive Learning

89 citations · 140 across the 9 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CL2021★ 7 cited

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…

cs.MM2021★ 4 cited

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…

cs.CL2021★ 1 cited

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…

cs.CV2021★ 3 cited

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…

cs.CL2021★ 89 cited

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…

cs.CL2021

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…