650 research outputs found
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images
Abstract—We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. In this paper, a novel framework based on Bayesian joint topic modelling is proposed, which differs significantly from the existing ones in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple object co-existence so that “explaining away ” inference can resolve ambiguity and lead to better learning and localisation. (2) Image backgrounds are shared across classes to better learn varying surroundings and “push out ” objects of interest. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Moreover, the Bayesian formulation enables the exploitation of various types of prior knowledge to compensate for the limited supervision offered by weakly labelled data, as well as Bayesian domain adaptation for transfer learning. Extensive experiments on the PASCAL VOC, ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of our Bayesian joint model for weakly supervised object localisation
Weakly Supervised Learning of Objects, Attributes and Their Associations
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”
Weakly Supervised Learning of Objects and Attributes.
PhDThis thesis presents weakly supervised learning approaches to directly
exploit image-level tags (e.g. objects, attributes) for comprehensive
image understanding, including tasks such as object localisation, image
description, image retrieval, semantic segmentation, person re-identification
and person search, etc. Unlike the conventional approaches which tackle
weakly supervised problem by learning a discriminative model, a generative
Bayesian framework is proposed which provides better mechanisms
to resolve the ambiguity problem. The proposed model significantly differentiates
from the existing approaches in that: (1) All foreground object
classes are modelled jointly in a single generative model that encodes multiple
objects co-existence so that “explaining away” inference can resolve
ambiguity and lead to better learning. (2) Image backgrounds are shared
across classes to better learn varying surroundings and “push out” objects
of interest. (3) the Bayesian formulation enables the exploitation of various
types of prior knowledge to compensate for the limited supervision
offered by weakly labelled data, as well as Bayesian domain adaptation
for transfer learning.
Detecting objects is the first and critical component in image understanding
paradigm. Unlike conventional fully supervised object detection
approaches, the proposed model aims to train an object detector
from weakly labelled data. A novel framework based on Bayesian latent
topic model is proposed to address the problem of localisation of objects
as bounding boxes in images and videos with image level object labels.
The inferred object location can be then used as the annotation to train a
classic object detector with conventional approaches.
However, objects cannot tell the whole story in an image. Beyond detecting
objects, a general visual model should be able to describe objects
and segment them at a pixel level. Another limitation of the initial model is
that it still requires an additional object detector. To remedy the above two
drawbacks, a novel weakly supervised non-parametric Bayesian model is
presented to model objects, attributes and their associations automatically
from weakly labelled images. Once learned, given a new image, the proposed
model can describe the image with the combination of objects and
attributes, as well as their locations and segmentation.
Finally, this thesis further tackles the weakly supervised learning problem
from a transfer learning perspective, by considering the fact that there
are always some fully labelled or weakly labelled data available in a related
domain while only insufficient labelled data exist for training in the
target domain. A powerful semantic description is transferred from the existing
fashion photography datasets to surveillance data to solve the person
re-identification problem
A weakly-supervised approach for discovering common objects in airport video surveillance footage
Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper we focus on object discovery in video sequences by using sets of unlabelled data. Thus, we present an approach based on the use of two region proposal algorithms (a pretrained Region Proposal Network and an Optical Flow Proposal) to produce regions of interest that will be grouped using a clustering algorithm. Therefore, our system does not require the collaboration of a human except for assigning human understandable labels to the discovered clusters. We evaluate our approach in a set of videos recorded at the outdoor area of an airport where the aeroplanes park to load passengers and luggage (apron area).
Our experimental results suggest that the use of an unsupervised approach is valid for automatic object discovery in video sequences, obtaining a CorLoc of 86.8 and a mAP of 0.374 compared to a CorLoc of 70.4 and mAP of 0.683 achieved by a supervised Faster R-CNN trained and tested on the same dataset.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Unsupervised learning of generative topic saliency for person re-identification
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201
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