As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Abstract Knowledge distillation (KD) is an effective training strategy to improve the
lightweight student models under the guidance of cumbersome teachers. However, the large …

Weakly supervised object localization (WSOL) is a challenging problem when given image
category labels but requires to learn object localization models. Optimizing a convolutional …

Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …

Abstract Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM) plus …

Weakly supervised dense object localization (WSDOL) relies generally on Class Activation
Map** (CAM), which exploits the correlation between the class weights of the image …

Weakly supervised object localization (WSOL) aims to localize objects by only utilizing
image-level labels. Class activation maps (CAMs) are the commonly used features to …

Weakly supervised object localization (WSOL) remains challenging when learning object
localization models from image category labels. Conventional methods that discriminatively …

Acquiring sufficient ground-truth supervision to train deep visual models has been a
bottleneck over the years due to the data-hungry nature of deep learning. This is …

Seed area generation is usually the starting point of weakly supervised semantic
segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label …