Techniques that integrate neuroscience and information science benefit both fields. Many related models have been proposed in computer vision; however, in general, the robustness and recognition precision are still key problems in object recognition models. In this paper, inspired by the process by which humans recognize objects and its biological mechanisms, a new integrated and dynamic framework is proposed that mimics the semantic extraction, concept formation and feature re-selection found in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learned from episodic features extracted from raw images using a deep neural network; (2) Integrated concept formation: Concepts are formed using the local semantic information and structural information is learned through a network; (3) Feature re-selection: When ambiguity is detected during the recognition process, distinctive features based on the differences between the ambiguous candidates are re-selected for recognition. Experimental results on four datasets show that—compared with other methods—the new proposed model is more robust and achieves higher precision for visual recognition, especially when the input samples are semantically ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.

Graph matching is a fundamental problem in theoretical computer science and artificial intelligence, and lays the foundation for many computer vision and machine learning tasks. Approximate algorithms are necessary for graph matching due to its NP-complete nature. Inspired by the usage in network-related tasks, random walk is generalized to graph matching as a type of approximate algorithm. However, it may be inappropriate for the previous random walk-based graph matching algorithms to utilize continuous techniques without considering the discrete property. In this paper, we propose a novel random walk-based graph matching algorithm by incorporating both continuous and discrete constraints in the optimization process. Specifically, after interpreting graph matching by random walk, the continuous constraints are directly embedded in the random walk constraint in each iteration. Further, both the assignment matrix (vector) and the pairwise similarity measure between graphs are iteratively updated according the discrete constraints, which automatically leads the continuous solution to the discrete domain. Comparisons on both synthetic and real-world data demonstrate the effectiveness of the proposed algorithm.

Ship detection in synthetic aperture radar (SAR) images faces challenges such as strong background interference, varying ship appearance, and distribution and high real-time requirements. Although attention-based deep learning methods dominate this field, the design of lightweight models with efficient attention mechanisms capable of addressing the aforementioned challenges remains underexplored. To address this issue, we propose a lightweight SAR ship detection model named LSDFormer, which is built upon the MetaFormer architecture and consists of an efficient multiattention-enhanced backbone and neck and a structural reparameterization (SR)-enhanced head. We employ two lightweight modules for the backbone and neck: a PoolFormer-based feature extraction module with efficient channel modulation attention is proposed to enhance ship features and suppress background interference, and a downsampling module using efficient channel aggregation attention and group convolutions is introduced to enrich ship features. The position-sensitive attention from YOLOv11 is also introduced to handle variations in ship appearance and distribution. These three attentions are integrated into an efficient multiattention mechanism. Furthermore, an SR-based detection branch is proposed for the head of LSDFormer, which enhances ship features while reducing model complexity. Extensive experiments on SSDD and HRSID datasets demonstrate the superiority and effectiveness of LSDFormer, achieving AP50 of $\mathbf {98.5\pm 0.4\%}$ and $\mathbf {92.8\pm 0.2\%}$, respectively, with only $\mathbf {1.5}$ M parameters and $\mathbf {4.1}$ GFLOPs. The average processing time per image is $\mathbf {4.9}$ ms on SSDD and $\mathbf {4.2}$ ms on HRSID, confirming its real-time performance.