Learning Local Semantic Region Activations for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to train instance-level locators by exploiting accessible image-level labels. By multiplying channel-wise features with classification weights and then adding them together, most prior works follow the pipeline of the Class Activation Map (CAM) to collect the semantic responses, thereby highlighting regions that contribute to class prediction to achieve WSOL. However, CAM-based methods treat the class contributions of all pixel positions in a channel equally and assign dominant weights for the discriminative channels biasedly. This fails to express the fine-grained pixel-level semantic response of each channel and model the complex contextual relations between channels, resulting in the mixup of the activation value between non-discriminative foreground regions and the background. To alleviate these issues, we present a Local Semantic activation enhancement and Global Spatial correlation mining network (LSGS-Net) for accurate WSOL. Specifically, we first propose a local activation generation module to explicitly learn the semantic response of each pixel position from channels. Then, we design a regularization loss to supervise the consistency between similar local activations, which utilizes the cross-image information to improve the accuracy of local activations. We further propose a K-nearest Neighbors graph module to capture the spatial correlation between different local activations, which can adaptively assign more proper weights when fusing all local activation. In the inference stage, the bounding box will be determined with a foreground threshold. Extensive experiments show that LSGS-Net achieves significant and consistent improvement with various backbones on the CUB, ILSVRC, and OpenImages benchmarks, with a 97.5% and 75.3% GT-Known LOC on CUB and ILSVRC, respectively. For segmentation quality on OpenImages, LSGS-Net already exceeds the SOTA method by 1.2% pIoU and 1.9% PxAP.