Computer Vision and Pattern Recognition Based on Machine Learning

Dear Colleagues,

Computer vision plays a pivotal role in modern intelligent systems, with widespread applications in medical diagnostics, industrial automation, remote sensing, autonomous driving, and beyond. Tasks such as image recognition, object detection, semantic segmentation, and 3D pose estimation are fundamental to these domains. However, the increasing complexity of vision models and the growing demand for real-time, resource-efficient solutions necessitate advancements in data-efficient and model-efficient learning.

This Special Issue focuses on cutting-edge research in efficient computer vision algorithms, covering both data efficiency and model efficiency. In data-efficient learning, the explored techniques reduce reliance on large-scale labelled datasets, including few-shot learning, transfer learning, domain adaptation, and self-supervised pre-training for downstream task adaptation. In model-efficient learning, the emphasis lies on lightweight architectures, neural architecture search (NAS), model compression (e.g., pruning, quantization, distillation), and the efficient design of convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid architectures.

 This Special Issue welcomes original research and reviews addressing efficiency challenges in computer vision. Other potential applications may include, but are not limited to, the following:

 • Medical imaging (e.g., low-data lesion detection);

• Industrial inspection (e.g., defect recognition with limited samples);

• Autonomous systems (e.g., real-time object tracking);

• Remote sensing (e.g., efficient land-cover segmentation). 

In summary, we welcome studies on the following topics:

1. Data-Efficient Learning

      - Few-shot/zero-shot learning for vision tasks;  

      - Transfer learning and domain adaptation;  

      -Self-supervised and weakly supervised learning;  

      - Active learning and annotation-efficient methods.

2. Model-Efficient Design  

      -Efficient CNN and transformer architectures;  

      - Neural architecture search (NAS) for efficient models;  

      - Model compression (pruning, quantization, knowledge distillation);  

      - Dynamic or adaptive inference for computational savings.

3. Efficient Vision Tasks

      -Real-time object detection and segmentation;

      - Efficient depth estimation and 3D reconstruction

      - Low-latency video analysis (e.g., action recognition);

      - Energy-efficient deployment on edge devices.

4. Applications and Case Studies

   - Efficient vision systems for healthcare, robotics, or agriculture;

   - Benchmarks and datasets for evaluating efficiency;

   - Hardware-aware algorithm design (e.g., for mobile/embedded devices).

Dr. Tao Wang
Guest Editor

Manuscript Submission Information

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• computer vision
• architecture design
• data-efficient learning
• image processing