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Shao Zeng

dblp:297/6891 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Security and privacy · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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
Transfer learning and domain adaptation · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Transfer learning and domain adaptation › fine-tuning
unsupervised fine-tuning
0.912025
From Enhancement to Understanding: Build a Generalized Bridge for Low-Light Vision via Semantically Consistent Unsupervised Fine-Tuning · ICCV 2025
Image and video processing › image enhancement
low-light image enhancement
0.912025
From Enhancement to Understanding: Build a Generalized Bridge for Low-Light Vision via Semantically Consistent Unsupervised Fine-Tuning · ICCV 2025

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

unsupervised fine-tuning · 1.7semantic consistency · 1.7
YearPublicationVenuePosition
2026 Unlocking the candidates: Beam-aware reasoning for audio-visual speech recognition
Shao Zeng, Tianjun Gu, Shouhong Ding, Xin Tan 0002, Yang Gao 0001
Pattern Recognit.6
2025 From Enhancement to Understanding: Build a Generalized Bridge for Low-Light Vision via Semantically Consistent Unsupervised Fine-Tuning
Shao Zeng, Tianjun Gu, Zhizhong Zhang 0001, Shouhong Ding, Jun Wang 0006, Xin Tan 0002, Yuan Xie 0006, Lizhuang Ma
ICCV2
2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization
abstract
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.
Caiyong Wang, Yunlong Wang 0003, Kunbo Zhang, Jawad Muhammad, Qi Zhang 0015, Qichuan Tian, Zhaofeng He 0001, Zhenan Sun, Tianbao Liu, Wei Yang 0006, Dongliang Wu, Yingfeng Liu, Ruiye Zhou, Huihai Wu, Junbao Wang, Wantong Xiong, Xueyu Shi, Shao Zeng, Peihua Li, Huijie Wu, Xinhui Zhang, Menghan Zhang, Fadi Boutros, Naser Damer, Arjan Kuijper, Juan E. Tapia, Andres Valenzuela, Christoph Busch 0001, Gourav Gupta, Kiran B. Raja, Xi Wu 0004, Xiaojie Li 0001, Jingfu Yang, Hongyan Jing, Xin Wang 0045, Bin Kong 0001, Youbing Yin, Qi Song 0001, Siwei Lyu, Shu Hu 0001, Leon Premk, Matej Vitek, Vitomir Struc, Peter Peer, Jalil Nourmohammadi-Khiarak, Farhang Jaryani, Samaneh Salehi Nasab, Seyed Naeim Moafinejad, Yasin Amini, Morteza Noshad
IJCB22