The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

Hospedales, TM

Shi, Z

Xiang, T

Yang, Y

English

arXiv

Queen Mary Research Online

A discriminative latent model of object classes and attributes. In: ECCV

A joint learning framework for attribute models and object descriptions. In: ICCV

A new svm approach to multi-instance multi-label learning. In:

A unified probabilistic approach modeling relationships between attributes and objects. In: ICCV

A.: Large-scale image annotation by efficient and robust kernel metric learning. In:

A.: Multi-attribute queries: To merge or not to merge? In: CVPR

A.K.: Tag completion for image retrieval. TPAMI

Actively selecting annotations among objects and attributes. In:

Aggregating local image descriptors into compact codes. TPAMI

Attribute adaptation for personalized image search. In: ICCV

Attribute dominance: What pops out? In:

Attribute learning for understanding unstructured social activity. In: ECCV

Attribute learning in large-scale datasets. In: ECCV

Attribute-based classification for zeroshot visual object categorization.

Baby talk: Understanding and generating simple image descriptions. In: CVPR

Bayesian joint topic modelling for weakly supervised object localisation. In: ICCV

C.G.M.: Evaluating color descriptors for object and scene recognition. TPAMI

C.J.: LIBLINEAR: A library for large linear classification. JMLR

Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In: CVPR

Contour detection and hierarchical image segmentation. TPAMI

Deformable part descriptors for fine-grained recognition and attribute prediction. In: ICCV

Describing clothing by semantic attributes. In: ECCV

Describing objects by their attributes. In: CVPR

Describing people: A poselet-based approach to attribute classification. In: ICCV

Image ranking and retrieval based on multiattribute queries. In: CVPR

Joint learning of visual attributes, object classes and visual saliency. In: ICCV

Latent dirichlet allocation models for image classification. TPAMI

Latent dirichlet allocation. JMLR

Learning beautiful (and ugly) attributes. In: BMVC

Neil: Extracting visual knowledge from web data. In:

Object detection with discriminatively trained part based models. TPAMI

Recognition using visual phrases. In: CVPR

S.C.: Weakly supervised learning for attribute localization in outdoor scenes. In: CVPR

Sun attribute database: Discovering, annotating, and recognizing scene attributes. In: CVPR

T.E.: Multi-attribute spaces: Calibration for attribute fusion and similarity search. In: CVPR

T.L.: From large scale image categorization to entry-level categories. In: ICCV

The indian buffet process: An introduction and review. JMLR

Towards total scene understanding:classification, annotation and segmentation in an automatic framework. In: CVPR

Weakly supervised localization and learning with generic knowledge.

Y.F.: Multi-instance multi-label learning.

Y.W.: Variational inference for the indian buffet process.

Weakly Supervised Learning of Objects, Attributes and Their Associations

Zhiyuan Shi

Yongxin Yang

Timothy M. Hospedales

Tao Xiang

Crossref

Abstract. When humans describe images they tend to use combina-tions of nouns and adjectives, corresponding to objects and their as-sociated attributes respectively. To generate such a description auto-matically, one needs to model objects, attributes and their associations. Conventional methods require strong annotation of object and attribute locations, making them less scalable. In this paper, we model object-attribute associations from weakly labelled images, such as those widely available on media sharing sites (e.g. Flickr), where only image-level la-bels (either object or attributes) are given, without their locations and associations. This is achieved by introducing a novel weakly supervised non-parametric Bayesian model. Once learned, given a new image, our model can describe the image, including objects, attributes and their as-sociations, as well as their locations and segmentation. Extensive exper-iments on benchmark datasets demonstrate that our weakly supervised model performs at par with strongly supervised models on tasks such as image description and retrieval based on object-attribute associations

CiteSeerX

Weakly Supervised Learning of Objects, Attributes and their Associations

When humans describe images they tend to use combinations of nouns and adjectives, corresponding to objects and their associated attributes respectively. To generate such a description automatically, one needs to model objects, attributes and their associations. Conventional methods require strong annotation of object and attribute locations, making them less scalable. In this paper, we model object-attribute associations from weakly labelled images, such as those widely available on media sharing sites (e.g. Flickr), where only image-level labels (either object or attributes) are given, without their locations and associations. This is achieved by introducing a novel weakly supervised non-parametric Bayesian model. Once learned, given a new image, our model can describe the image, including objects, attributes and their associations, as well as their locations and segmentation. Extensive experiments on benchmark datasets demonstrate that our weakly supervised model performs at par with strongly supervised models on tasks such as image description and retrieval based on object-attribute associations. Â© 2014 Springer International Publishing

Shi, Zhiyuan

Yang, Yongxin

Hospedales, Timothy M

Xiang, Tao

University of Surrey

Weakly supervised learning of objects, attributes and their associations

Shi, Zhiyuan

Yang, Yongxin

Hospedales, Timothy M

Xiang, Tao

Surrey Research Insight

Abstract. When humans describe images they tend to use combinations of nouns and adjectives, corresponding to objects and their associated attributes respectively. To generate such a description automatically, one needs to model objects, attributes and their associations. Conventional methods require strong annotation of object and attribute locations, making them less scalable. In this paper, we model objectattribute associations from weakly labelled images, such as those widely available on media sharing sites (e.g. Flickr), where only image-level labels (either object or attributes) are given, without their locations and associations. This is achieved by introducing a novel weakly supervised non-parametric Bayesian model. Once learned, given a new image, our model can describe the image, including objects, attributes and their associations, as well as their locations and segmentation. Extensive experiments on benchmark datasets demonstrate that our weakly supervised model performs at par with strongly supervised models on tasks such as image description and retrieval based on object-attribute associations

http://zhiyshi.files.wordpress.com/2014/08/eccv2014.pdf

Weakly Supervised Learning of Objects, Attributes and Their Associations

Abstract

Similar works

Full text

Available Versions

Queen Mary Research Online

Crossref

CiteSeerX

University of Surrey

Surrey Research Insight

CiteSeerX