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Chuhong Wang

dblp:283/3177 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
—ORCID · conflict

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

Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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
Segmentation and scene understanding · 100%
Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 77% Immersive interaction · 23%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding › instance segmentation
camouflaged object segmentation
1.012026
Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset · IEEE Trans. Image Process. 2026
Haptics and multimodal interaction › olfactory interfaces
olfactory display
0.912025
Multi-scent olfactory display with visual and sound interaction · Int. J. Hum. Comput. Stud. 2025
Computer vision › Segmentation and scene understanding › prompt-based segmentation
segment anything model
0.312026
Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset · IEEE Trans. Image Process. 2026

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

segment anything model · 1.0frequency domain integration · 1.0channel balance optimization · 1.0
YearPublicationVenuePosition
2026 Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset
abstract
With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our UCIS-SAM includes three key modules. First, the Channel Balance Optimization Module (CBOM) enhances channel characteristics to improve underwater feature learning, effectively addressing the model's limited understanding of underwater environments. Second, the Frequency Domain True Integration Module (FDTIM) is proposed to emphasize intrinsic object features and reduce interference from camouflage patterns, enhancing the segmentation performance of camouflaged objects blending with their surroundings. Finally, the Multi-scale Feature Frequency Aggregation Module (MFFAM) is designed to strengthen the boundaries of low-contrast camouflaged instances across multiple frequency bands, improving the model's ability to achieve more precise segmentation of camouflaged objects. Extensive experiments on the proposed UCIS4K and public benchmarks show that our UCIS-SAM outperforms state-of-the-art approaches. The code and dataset are released at https://github.com/wchchw/UCIS4K.
Chuhong Wang, Hua Li 0012, Chongyi Li, Huazhong Liu, Xiongxin Tang, Sam Kwong
IEEE Trans. Image Process.1
2025 Multi-scent olfactory display with visual and sound interaction
abstract
Historically, research into olfaction and its related technologies has developed at a slower pace than work into other human senses. Recently, the impact of COVID-19 has resulted in temporary smell loss for many people and has brought the importance of olfaction to the public's attention. This has prompted ideas on how we might utilise olfaction and its related technologies better in our daily lives. However, the limited research in this area means the public has been restricted to devices as simple as air fresheners and scented candles.
Chuhong Wang, Emma Daulton, James Anthony Covington
Int. J. Hum. Comput. Stud.1
2021 A noise-suppressing Newton-Raphson iteration algorithm for solving the time-varying Lyapunov equation and robotic tracking problems
Guan-Cheng Wang 0002, Haoen Huang 0001, Limei Shi, Chuhong Wang, Dongyang Fu, Long Jin 0001, Xiuchun Xiao
Inf. Sci.4