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

Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene
understanding. Recent approaches have achieved excellent results on very large …

Conventional training of a deep CNN based object detector demands a large number of
bounding box annotations, which may be unavailable for rare categories. In this work we …

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

Few-shot learning, ie, learning novel concepts from few examples, is fundamental to
practical visual recognition systems. While most of existing work has focused on few-shot …

Can we detect common objects in a variety of image domains without instance-level
annotations? In this paper, we present a framework for a novel task, cross-domain weakly …

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 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 …

In this paper, we introduce a new problem, named audio-visual video parsing, which aims to
parse a video into temporal event segments and label them as either audible, visible, or …