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20192026
most citedNatural Language Video Localization: A Revisit in Span-based Question Answering Framework

93 citations · 112 across the 8 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 6 cited

Towards Debiasing Temporal Sentence Grounding in Video

Hao Zhang, Aixin Sun, Wei Jing +1

The temporal sentence grounding in video (TSGV) task is to locate a temporal moment from an untrimmed video, to match a language query, i.e., a sentence. Without considering bias i…

cs.LG2021

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3

The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-…

cs.CV2021

Domain Generalization for Vision-based Driving Trajectory Generation

Yunkai Wang, Dongkun Zhang, Yuxiang Cui +5

One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for visi…

cs.CL2021

Parallel Attention Network with Sequence Matching for Video Grounding

Hao Zhang, Aixin Sun, Wei Jing +3

Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequ…

cs.CL2021

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★ 93 cited

Natural Language Video Localization: A Revisit in Span-based Question Answering Framework

Hao Zhang, Aixin Sun, Wei Jing +3

Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve th…