Special Issue on Human Visual Saliency and Artificial Neural Attention in Deep Learning
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Human visual system canprocess large amounts of visual information (108-109bits per second) in parallel. Such astonishing ability is based on the visual attention mechanism which allows human beings to selectively attend to the most informative and characteristic parts of a visual stimulus rather than the whole scene. Modeling visual saliency is a long-term core topic in cognitive psychology and computer vision community. Further, understanding human gaze behavior during social scenes is essential for understanding Human-Human Interactions (HHIs) and enabling effective and natural Human-Robot Interactions (HRIs). In addition, the selective mechanism of human visual system inspires the development of differentiable neural attention in neural networks. Neural networks with attention mechanism are able to automatically learn to selectively focus on sections of input, which have shown wide success in many neural language processing and mainstream computer vision tasks, such asneural machine translation, sentence summarization, image caption generation, visual question answering, and action recognition. The visual attention mechanism also boosts biologically-inspired object recognition, including salient object detection, object segmentation, and object classification.
The list of possible topics includes, but is not limited to:
Visual attention prediction during static/dynamic scenes
Computational models for saliency/co-saliency detection in images/videos
Computational models for social gaze, co-attention and gaze-following behavior