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Xuelong Wang

dblp:155/3545 · DBLP profile ↗
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4ranked-venue papers
0as first author
3since 2021 · last 2027
—ORCID · conflict

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

Artificial intelligence and machine learning · 3 · 3 since 2021Applied, interdisciplinary, general and emerging computing · 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
Vision and language · 100%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Vision and language › multimodal dialogue
multimodal dialogue understanding
1.012026
JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations · ACL (1) 2026
Multimedia analysis and retrieval
multimodal benchmark
1.012026
JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations · ACL (1) 2026
Computer vision › Vision and language › vision-language model
multimodal large language model
0.312026
JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations · ACL (1) 2026

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

multimodal annotation · 2.0benchmark construction · 2.0
YearPublicationVenuePosition
2027 An interpretable causality- and topology-aware multi-expert system for network flow anomaly detection
Yuan Gao 0062, Xuelong Wang, Zhenguo Dong, Yong Zhang 0029, Yanyan Han
Expert Syst. Appl.2
2026 JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations
abstract
Xinyi Xu, Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou
ACL (1)7
2026 Dynamic adaptive parsing of temporal and cross-variable patterns for network state classification
Yuan Gao 0062, Xuelong Wang, Zhenguo Dong, Yong Zhang 0029
Expert Syst. Appl.2
2014 Reconstruction of the Gene Regulatory Network Involved in the Sonic Hedgehog Pathway with a Potential Role in Early Development of the Mouse Brain
abstract
The Sonic hedgehog (Shh) signaling pathway is crucial for pattern formation in early central nervous system development. By systematically analyzing high-throughput in situ hybridization data of E11.5 mouse brain, we found that Shh and its receptor Ptch1 define two adjacent mutually exclusive gene expression domains: Shh+Ptch1- and Shh-Ptch1+. These two domains are associated respectively with Foxa2 and Gata3, two transcription factors that play key roles in specifying them. Gata3 ChIP-seq experiments and RNA-seq assays on Gata3-knockdown cells revealed that Gata3 up-regulates the genes that are enriched in the Shh-Ptch1+ domain. Important Gata3 targets include Slit2 and Slit3, which are involved in the process of axon guidance, as well as Slc18a1, Th and Qdpr, which are associated with neurotransmitter synthesis and release. By contrast, Foxa2 both up-regulates the genes expressed in the Shh+Ptch1- domain and down-regulates the genes characteristic of the Shh-Ptch1+ domain. From these and other data, we were able to reconstruct a gene regulatory network governing both domains. Our work provides the first genome-wide characterization of the gene regulatory network involved in the Shh pathway that underlies pattern formation in the early mouse brain.
Xuelong Wang, Haifang Wang, Jun Yan 0004
PLoS Comput. Biol.2