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Shuailing Hao

dblp:371/6258 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, 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 · 50% Language models and text generation · 50%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Vision and language
image captioning
0.812024
Visual Language - Let the Product Say What You Want · AAAI 2024
Natural language and speech › Language models and text generation › text generation › data-to-text generation
product description generation
0.812024
Visual Language - Let the Product Say What You Want · AAAI 2024
Multimedia analysis and retrieval
cross-modal retrieval
0.812024
Visual Language - Let the Product Say What You Want · AAAI 2024
Multimedia analysis and retrieval › image retrieval
product retrieval
0.812024
Visual Language - Let the Product Say What You Want · AAAI 2024
Information retrieval
e-commerce search
0.212024
Visual Language - Let the Product Say What You Want · AAAI 2024

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

multimodal retrieval · 2.3cross-modal generation · 2.3
YearPublicationVenuePosition
2024 Visual Language - Let the Product Say What You Want
abstract
Visual Language is a multitasking on-line system focusing on e-commerce, which involves in generating accurate product descriptions for sellers and providing convenient product retrieval service for customers. To achieve this goal, the system adopts image description technology and multi-modal retrieval technology. By utilizing cross-modal generation technique, we could help sellers on rapid uploading products and customers on rapid retrieval, which could improve the experience of both sellers and customers.
Shuailing Hao, Jing Shan, Xiaoxu Song
AAAI2