Rethinking the Localization in Weakly Supervised Object Localization
This work addresses limitations in WSOL for computer vision applications, offering a more flexible and robust approach, though it is incremental as it builds on an existing pipeline.
The paper tackles the problem of localizing multiple objects in weakly supervised object localization by replacing single-class regression with a binary-class detector and introducing a weighted entropy loss to mitigate noise from pseudo bounding boxes, achieving improved performance on CUB-200-2011 and ImageNet-1K datasets.
Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the image-level supervision. Recently, dividing WSOL into two parts (class-agnostic object localization and object classification) has become the state-of-the-art pipeline for this task. However, existing solutions under this pipeline usually suffer from the following drawbacks: 1) they are not flexible since they can only localize one object for each image due to the adopted single-class regression (SCR) for localization; 2) the generated pseudo bounding boxes may be noisy, but the negative impact of such noise is not well addressed. To remedy these drawbacks, we first propose to replace SCR with a binary-class detector (BCD) for localizing multiple objects, where the detector is trained by discriminating the foreground and background. Then we design a weighted entropy (WE) loss using the unlabeled data to reduce the negative impact of noisy bounding boxes. Extensive experiments on the popular CUB-200-2011 and ImageNet-1K datasets demonstrate the effectiveness of our method.