Spatial attention for human-centric visual understanding: An Information Bottleneck method

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qiuxia Lai , Yongwei Nie , Yu Li , Hanqiu Sun , Qiang Xu
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引用次数: 0

Abstract

The selective visual attention mechanism in the Human Visual System (HVS) restricts the amount of information that reaches human visual awareness, allowing the brain to perceive high-fidelity natural scenes in real-time with limited computational cost. This selectivity acts as an “Information Bottleneck (IB)” that balances information compression and predictive accuracy. However, such information constraints are rarely explored in the attention mechanism for deep neural networks (DNNs). This paper introduces an IB-inspired spatial attention module for DNNs, which generates an attention map by minimizing the mutual information (MI) between the attentive content and the input while maximizing that between the attentive content and the output. We develop this IB-inspired attention mechanism based on a novel graphical model and explore various implementations of the framework. We show that our approach can yield attention maps that neatly highlight the regions of interest while suppressing the backgrounds, and are interpretable for the decision-making of the DNNs. To validate the effectiveness of the proposed IB-inspired attention mechanism, we apply it to various computer vision tasks including image classification, fine-grained recognition, cross-domain classification, semantic segmentation, and object detection. Extensive experiments demonstrate that it bootstraps standard DNN structures quantitatively and qualitatively for these tasks.
以人为本的视觉理解空间注意力:信息瓶颈法
人类视觉系统(HVS)中的选择性视觉注意力机制限制了到达人类视觉意识的信息量,使大脑能够以有限的计算成本实时感知高保真自然场景。这种选择性起到了 "信息瓶颈(IB)"的作用,在信息压缩和预测准确性之间取得了平衡。然而,在深度神经网络(DNN)的注意力机制中,这种信息约束很少被探索。本文介绍了受 IB 启发的 DNN 空间注意力模块,该模块通过最小化注意力内容与输入之间的互信息(MI),同时最大化注意力内容与输出之间的互信息(MI)来生成注意力地图。我们基于新颖的图形模型开发了这种受 IB 启发的注意力机制,并探索了该框架的各种实现方法。我们的研究表明,我们的方法可以生成注意力图,在抑制背景的同时突出感兴趣的区域,并且可以为 DNN 的决策提供解释。为了验证受 IB 启发的注意力机制的有效性,我们将其应用于各种计算机视觉任务,包括图像分类、细粒度识别、跨域分类、语义分割和物体检测。广泛的实验证明,在这些任务中,它能从定量和定性上引导标准 DNN 结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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