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

Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …

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

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

Weakly supervised learning with only coarse labels can obtain visual explanations of deep
neural network such as attention maps by back-propagating gradients. These attention …

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 …

Weakly supervised learning has emerged as a compelling tool for object detection by
reducing the need for strong supervision during training. However, major challenges …

Weakly supervised object detection (WSOD) is a challenging task when provided with image
category supervision but required to simultaneously learn object locations and object …

The widespread popularity of unmanned aerial vehicles enables an immense amount of
power line inspection data to be collected. It is an urgent issue to employ massive data …

Weakly supervised methods usually generate localization results based on attention maps
produced by classification networks. However, the attention maps exhibit the most …