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Danqian Liu

dblp:212/2966 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0003-2604-2113ORCID · reported

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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
Video understanding and tracking · 50% Segmentation and scene understanding · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › video analytics › behavior analysis
animal behavior analysis		 0.412020
SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis · IJCAI 2020
Computer vision › Segmentation and scene understanding
instance segmentation		 0.412020
SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis · IJCAI 2020
Bioinformatics and computational biology
behavioral analysis		 0.112020
SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis · IJCAI 2020

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

tracking · 0.9siamese network · 0.9segmentation · 0.9
YearPublicationVenuePosition
2020 SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis
abstract
Biologists often need to handle numerous video-based home-cage animal behavior analysis tasks that require massive workloads. Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis. In this system, a background-enhanced Siamese-based network with replaceable modular design ensures the flexibility and generalizability of the system, and a user-friendly interface makes it convenient to use for biologists. This real-time AI system will effectively reduce the burden on biologists.
Xi Chen 0031, Hao Zhai 0003, Danqian Liu, Weifu Li, Chaoyue Ding, Qiwei Xie, Hua Han 0001
 IJCAI3