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

Although there are well established object detection methods based on static images, their
application to video data on a frame by frame basis faces two shortcomings:(i) lack of …

Abstract We propose'Hide-and-Seek', a weakly-supervised framework that aims to improve
object localization in images and action localization in videos. Most existing weakly …

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

Of late, weakly supervised object detection is with great importance in object recognition.
Based on deep learning, weakly supervised detectors have achieved many promising …

Traditional techniques for emotion recognition have focused on the facial expression
analysis only, thus providing limited ability to encode context that comprehensively …

Recent advances in object detection are mainly driven by deep learning with large-scale
detection benchmarks. However, the fully-annotated training set is often limited for a target …

Manually annotating object bounding boxes is central to building computer vision datasets,
and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box …

We address weakly supervised action alignment and segmentation in videos, where only
the order of occurring actions is available during training. We propose Discriminative …

This work addresses the unsupervised adaptation of an existing object detector to a new
target domain. We assume that a large number of unlabeled videos from this domain are …