Abstract: Disclosed are a CAM-based weakly supervised object localization device and method. The device includes: a feature map extractor configured to extract a feature map of a last convolutional layer in a convolutional neural network (CNN) in a process of applying an image to the CNN; a weight vector binarization unit configured to first binarize a weight vector of a linear layer in a process of sequentially applying the feature map to a pooling layer that generates a feature vector and the linear layer that generates a class label; a feature map binarization unit configured to second binarize the feature map based on the first binarized weight vector; and a class activation map generation unit configured to generate a class activation map for object localization based on the second binarized feature map.

Abstract: A method of segmenting objects in an image using artificial intelligence includes obtaining, by an analysis device, an image containing at least one object; inputting the image acquired by the analysis device into a segmentation model; and segmenting, by the analysis device, objects in the acquired image based on values output by the segmentation model, wherein an image containing at least one object is used as learning data, the size of objects in the learning data is estimated as part of the learning process, different weightings are given to pixels of which the objects consist according to the estimated size of the objects, and the segmentation model is trained based on a loss function in which the given weightings are considered, a size-weighted loss function.

Abstract: Disclosed are a CAM-based weakly supervised object localization device and method. The device includes: a feature map extractor configured to extract a feature map of a last convolutional layer in a convolutional neural network (CNN) in a process of applying an image to the CNN; a weight vector binarization unit configured to first binarize a weight vector of a linear layer in a process of sequentially applying the feature map to a pooling layer that generates a feature vector and the linear layer that generates a class label; a feature map binarization unit configured to second binarize the feature map based on the first binarized weight vector; and a class activation map generation unit configured to generate a class activation map for object localization based on the second binarized feature map.