AI-Driven Sensing for Small Object Recognition

Dear Colleagues,

Small-object recognition is a fundamental research problem in the field of computer vision and sensing. It is difficult to detect small-objects due to many practical factors, such as indistinguishable features, low resolution, complicated backgrounds, and limited contextual information. The success of small-object recognition could enable many important tasks, such as object tracking, instance segmentation, image captioning, action recognition, and scene understanding. Small-object recognition also plays an important role in real-world scenarios, e.g., urban planning, robotic vision, autonomous driving, intelligent transportation, intelligent healthcare, military reconnaissance and surveillance, where objects of interest could be of a very small size. The integration of advanced artificial intelligence (AI) and sensing technologies could contribute potential solutions to this challenging task. As a result, this Special Issue aims to present the latest advances in deep models, algorithms, and applications that contribute to AI-driven small-object recognition. The main topics include (but are not limited to):

• Effective feature extraction for small-object recognition;
• Data augmentation for small-object recognition;
• Cross-/multi-modal learning for small-object recognition;
• Meta learning for small-object recognition;
• Explainable models for small-object recognition;
• Robust learning for small-object recognition;
• Novel benchmarks for small-object recognition;
• Novel applications for small-object recognition.

Prof. Dr. Jianqiang Li
Prof. Dr. Victor C. M. Leung
Dr. Houbing Song
Dr. Jie Chen
Guest Editors

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