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Linli Lin

dblp:331/1495 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Vision and language · 70% Video understanding and tracking · 23% Face, body and person analysis · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › dynamic scene analysis › video scene understanding
human-centric scene understanding
0.612022
PIC'22: 4th Person in Context Workshop · ACM Multimedia 2022
Computer vision › Vision and language
multimodal reasoning
0.612022
PIC'22: 4th Person in Context Workshop · ACM Multimedia 2022
Computer vision › Vision and language › video grounding
spatio-temporal video grounding
0.612022
PIC'22: 4th Person in Context Workshop · ACM Multimedia 2022
Computer vision › Vision and language
video grounding
0.612022
PIC'22: 4th Person in Context Workshop · ACM Multimedia 2022
Computer vision › Face, body and person analysis
person search
0.212022
PIC'22: 4th Person in Context Workshop · ACM Multimedia 2022

Methods — techniques the papers use, named apart from their topics

multimodal reasoning · 0.6dense video captioning · 0.6
YearPublicationVenuePosition
2022 PIC'22: 4th Person in Context Workshop
abstract
Understanding human and the surrounding context is crucial for the perception of the image and video. It benefits many related applications, such as person search, virtual tryon/makeup, abnormal action detection. In the proposed 4th Person in Context (PIC) workshop, to further promote the progress in the above-mentioned areas, we hold three human-centric perception and cognition challenges including Make-up Temporal Video Grounding (MTVG), Make-up Dense Video Caption (MDVC) and Human-centric Spatio-Temporal Video Grounding (HC-STVG). All the human-centric challenges focus on understanding the human behavior, interactions and relationships in video sequences, which requires understanding both visual and linguistic information, as well as complicated multimodal reasoning. The three sub-problems are complementary and collaboratively contribute to a unified human-centric perception and cognition solution.
Si Liu 0001, Qin Jin, Luoqi Liu, Zongheng Tang, Linli Lin
ACM Multimedia5