LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

• Published in: IEEE transactions on image processing Vol. 30; pp. 5875 - 5888
• Main Authors: Jiang, Peng-Tao, Zhang, Chang-Bin, Hou, Qibin, Cheng, Ming-Ming, Wei, Yunchao
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: class activation maps; Convolution; Feature maps; Image segmentation; Localization; Location awareness; Pixels; Reliability; Semantic segmentation; Semantics; Spatial resolution; Task analysis; Weakly-supervised object localization
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2021.3089943

The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for weakly-supervised tasks. However, due to the small spatial resolution of the final convolutional layer, such class activation maps often locate coarse regions of the target objects, limiting the performance of weakly-supervised tasks that need pixel-accurate object locations. Thus, we aim to generate more fine-grained object localization information from the class activation maps to locate the target objects more accurately. In this paper, by rethinking the relationships between the feature maps and their corresponding gradients, we propose a simple yet effective method, called LayerCAM. It can produce reliable class activation maps for different layers of CNN. This property enables us to collect object localization information from coarse (rough spatial localization) to fine (precise fine-grained details) levels. We further integrate them into a high-quality class activation map, where the object-related pixels can be better highlighted. To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation. Experiments demonstrate that the class activation maps generated by our method are more effective and reliable than those by the existing attention methods. The code will be made publicly available.