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Article
Computer Science, Artificial Intelligence
Xiaoxu Li et al.
Summary: Traditional deep learning-based image classification methods often struggle with recognizing new classes with few samples. The proposed ReNAP method incorporates an adaptive prototypical learning module into RelationNet, enabling more accurate representation and superior classification performance. The results demonstrate its effectiveness on four benchmark datasets. (c) 2022 Elsevier B.V. All rights reserved.
Review
Fisheries
Juan Li et al.
Summary: This article reviews the current application status, challenges, and future directions of DL in the field of aquatic animal recognition and detection. Key advances and applications of DL in the recognition and detection of aquatic animals are summarized. DL has significant application value in the field of aquatic animal recognition and detection.
REVIEWS IN AQUACULTURE
(2023)
Article
Computer Science, Artificial Intelligence
Zonghui Guo et al.
Summary: This paper introduces a method of using Transformer to solve the image harmonization problem. By leveraging the Transformer model's ability to model long-range context dependencies, the foreground light is adjusted to be compatible with the background light while preserving the structure and semantics of the image. Experimental results demonstrate the superiority of this method in image harmonization as well as other vision and graphics tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Eko Prasetyo et al.
Summary: This paper proposes an image-based fish classification system using a Convolutional Neural Network (CNN) that combines low-level and high-level features using a multi-level residual network strategy, and introduces new techniques in the CNN architecture to improve performance. Experimental results show that the system performs well on fish image datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Civil
Peng Liu et al.
Summary: The vision system plays a crucial role in autonomous driving systems, but the presence of scattering media can seriously degrade visual data and affect the reliability of the systems. This study proposes a method that employs dense blocks and attention mechanism to achieve excellent performance in scattering medium imaging optimization, using training data from a weakly supervised model.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Mutasem K. Alsmadi et al.
Summary: Fish classification is a widely studied problem in the fields of image segmentation, pattern recognition, and information retrieval. This study compares and evaluates various preprocessing methods, feature extraction techniques, and classifiers, and reviews the use of relevant databases. By collecting recent research works, it provides guidance for future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xiu-Shen Wei et al.
Summary: Fine-grained image analysis is a longstanding and fundamental problem in computer vision, and has seen remarkable progress driven by deep learning. This field involves analyzing visual objects from subordinate categories and presents challenges due to small inter-class and large intra-class variation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Engineering, Civil
Zhensheng Shi et al.
Summary: This article presents a method for marine organism detection based on visual attention and relation mechanism. By applying an improved attention-relation module on an efficient marine organism detector, the discrimination of organisms in complex underwater environments can be enhanced. The proposed method outperforms state-of-the-art approaches on experimental data sets.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Fuzhen Zhuang et al.
Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.
PROCEEDINGS OF THE IEEE
(2021)
Review
Fisheries
Shili Zhao et al.
Summary: Machine learning technology has been extensively applied in aquaculture in recent years, covering areas such as fish biomass evaluation, fish identification and classification, behavioral analysis, and prediction of water quality parameters. Current issues in aquaculture have been highlighted, and development trends have been considered.
Article
Computer Science, Artificial Intelligence
Xiaoxu Li et al.
Summary: The proposed Bi-Similarity Network (BSNet) utilizes two similarity measures to learn more discriminative and less similarity-biased features from few shots of fine-grained images, significantly improving model generalization ability. Extensive experiments show substantial improvement on several fine-grained image benchmark datasets, demonstrating the effectiveness of the approach. The codes for the model are available at: https://github.com/PRIS-CV/BSNet.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaomeng Li et al.
Article
Fisheries
Alaa Tharwat et al.
FISHERIES RESEARCH
(2018)
Review
Automation & Control Systems
Bo Zhao et al.
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
(2017)
