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20182022
most citedFederated Mutual Learning

71 citations · 163 across the 26 of their papers we have counts for

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18 papers · 1 filter

cs.CV2022

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…

cs.CV2021

Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding

Daizong Liu, Xiaoye Qu, Pan Zhou

A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence…

cs.CV2021

Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos

Daizong Liu, Xiaoye Qu, Jianfeng Dong +1

We address the problem of temporal sentence localization in videos (TSLV). Traditional methods follow a top-down framework which localizes the target segment with pre-defined segme…

cs.CV20213 cited

Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

Zhikang Zou, Xiaoye Qu, Pan Zhou +4

Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applic…

cs.CV20215 cited

A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning

Pan Zhou, Caiming Xiong, Xiao-Tong Yuan +1

For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assig…

cs.CV202115 cited

Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

Daizong Liu, Xiaoye Qu, Jianfeng Dong +5

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence quer…