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Weakly supervised object localization (WSOL) is a challenging task that aims to localize objects in images using only image-level labels. Despite the widespread use of WSOL methods based on class activation mapping (CAM), such methods do not consider that the network may overly focus on local regions of the most interesting objects during localization, thus neglecting the overall information. To address this issue, we introduces an additional attention branch for convolutional neural networks (CNNs) that utilizes the attention mechanism of multi-layer perceptron (MLP) to enhance the network's learning of global information and supervise the feature learning of CNNs online through knowledge distillation, thereby improving the localization accuracy of WSOL. Specifically, we designs a new loss function using the generated features to combine with contrastive learning, effectively dividing the foreground and background of the image to provide more accurate pseudo-labels for subsequent classification and localization tasks. In the experiments, we tested our method on the CUB-200-2011 dataset and compared it with existing methods. The experimental results show that our method achieves good performance in WSOL tasks. [ABSTRACT FROM AUTHOR]

Knowledge distillation (KD) builds a lightweight Student Model (SM) and trains it to approximate a large Teacher Model (TM) by exploring knowledge learned by the TM, which shows effectiveness to train lightweight CNN models. However, training a small SM to achieve better performance remains a challenging problem. Recent researches on human learning behaviors show that both the knowledge from teachers and the knowledge learning processes of teachers are significant for students. Inspired by this characteristic, in this paper, we propose a new perspective, called Progressive Mimic Learning (PML), to train lightweight CNN models by mimicking the learning trajectory of the TM. In order to obtain a more powerful SM, the useful hints in the learning process of the TM are explored. To start with, the TM learning process is divided into multiple stages, and the last state of the TM in each stage is recorded as a landmark. The learning trajectory of the TM is composed of these landmarks. Then, a landmark loss is defined to constrain the SM to progressively mimic the learning process of the TM, by employing landmarks in the learning trajectory as a training hint of the SM. Several experiments are conducted on four benchmark data sets, CIFAR-10, CIFAR-100, Fashion-MNIST, and ImageNet-10, to investigate the performance of the PML. The results show that the PML can make SMs generate more accurate predictions than SMs trained by its counterparts. [ABSTRACT FROM AUTHOR]