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

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …

Abstract Weakly Supervised Object Localization (WSOL) techniques learn the object
location only using image-level labels, without location annotations. A common limitation for …

Making a precise annotation in a large dataset is crucial to the performance of object
detection. While the object detection task requires a huge number of annotated samples to …

Despite remarkable progress, weakly supervised segmentation methods are still inferior to
their fully supervised counterparts. We obverse that the performance gap mainly comes from …

In this work, we propose Adversarial Complementary Learning (ACoL) to automatically
localize integral objects of semantic interest with weak supervision. We first mathematically …

Deep learning in the presence of noisy annotations has been studied extensively in
classification, but much less in segmentation tasks. In this work, we study the learning …

Weakly Supervised Object Detection (WSOD), using only image-level annotations to train
object detectors, is of growing importance in object recognition. In this paper, we propose a …

Sketches are highly expressive, inherently capturing subjective and fine-grained visual
cues. The exploration of such innate properties of human sketches has, however, been …