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Learning Features and Parts for Fine-Grained Recognition
2014
2014 22nd International Conference on Pattern Recognition
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The part detectors are learned in a fully unsupervised manner, based on the insight that images with similar poses can be automatically discovered for fine-grained classes in the same domain. ...
We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. ...
Suppose we have a collection of n object parts with associated part detectors, which we assume for now have already been trained. ...
doi:10.1109/icpr.2014.15
dblp:conf/icpr/KrauseGDLF14
fatcat:mh7b3p4glzgjnkjdlyavbfd4xy
Ensemble of Part Detectors for Simultaneous Classification and Localization
[article]
2017
arXiv
pre-print
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However, automatic discovery of discriminative parts without object/part-level annotations is challenging. ...
This paper proposes a discriminative mid-level representation paradigm based on the responses of a collection of part detectors, which only requires the image-level labels. ...
LEARNING PART DETECTORS In this section, we target at learning a collection of discriminative part detectors automatically for image representation. ...
arXiv:1705.10034v1
fatcat:qcrjsblvg5bobptvq5px7g3y54
Learning Collections of Part Models for Object Recognition
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. ...
We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. ...
Acknowledgements This research is supported in part by ONR MURI grant N000141010934, NSF CAREER award 10-53768, and NSF award IIS 09-04209. ...
doi:10.1109/cvpr.2013.126
dblp:conf/cvpr/EndresSJH13
fatcat:icbxdvmmmbct7b5ej76ydhlkk4
Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning
2010
2010 20th International Conference on Pattern Recognition
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What is more, an online learning process is proposed to learn discriminative human observations, including discriminative interest points and color patches, to effectively track each human when even more ...
To cope with the difficulties it presents, an offline boosted multiview upper-body detector is used to automatically initialize a new human trajectory and is capable of dealing with partial human occlusions ...
ACKNOWLEDGMENT This work is supported in part by National Basic Research Program of China (2006CB303102), Beijing Educational Committee Program (YB20081000303), and it is also supported by a grant from ...
doi:10.1109/icpr.2010.420
dblp:conf/icpr/XingAL10
fatcat:4rkp4xkimjbtbfnuwz2pr6jffa
Learning Co-occurrence of Local Spatial Strokes for Robust Character Recognition
2014
IEICE transactions on information and systems
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High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. ...
In this paper, we propose a representation method based on local spatial strokes for scene character recognition. ...
Framework The proposed method consists of four parts: (1) labeling key points for character training images and choosing discriminative strokes for every character; (2) collecting stroke training samples ...
doi:10.1587/transinf.e97.d.1937
fatcat:xqg6ihrkifb3fhfts46tifbf44
Picking Deep Filter Responses for Fine-Grained Image Recognition
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This paper proposes an automatic finegrained recognition approach which is free of any object / part annotation at both training and testing stages. ...
The first picking step is to find distinctive filters which respond to specific patterns significantly and consistently, and learn a set of part detectors via iteratively alternating between new positive ...
Learning Part Detectors In this section, we target at learning a collection of discriminative detectors that automatically discover discriminative object / parts. ...
doi:10.1109/cvpr.2016.128
dblp:conf/cvpr/ZhangXZLT16
fatcat:uaxknyseknfljdm2dnhlzjw6bq
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints ...
This is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual ...
We are grateful to the anonymous reviewers for their constructive comments. ...
doi:10.1109/cvpr.2014.487
dblp:conf/cvpr/AubryMERS14
fatcat:y4x6gxrcebglxcoqjmbcx3rrgq
Fine-Grained Crowdsourcing for Fine-Grained Recognition
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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This necessitates the use of stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. ...
Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. ...
Acknowledgments We thank Alexandre Alahi, Michelle Greene, Olga Russakovsky, Bangpeng Yao, and anonymous reviewers for their comments. ...
doi:10.1109/cvpr.2013.81
dblp:conf/cvpr/DengK013
fatcat:4n4brjjy7fb3pbufe7apode4ea
Human Attribute Recognition by Rich Appearance Dictionary
2013
2013 IEEE International Conference on Computer Vision
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We present a part-based approach to the problem of human attribute recognition from a single image of a human body. ...
To this end, we propose to learn a rich appearance part dictionary of human with significantly less supervision by decomposing image lattice into overlapping windows at multi-scales and iteratively refining ...
In this paper, we learn the dictionary of discriminative parts for the task of attribute recognition directly from training images. ...
doi:10.1109/iccv.2013.95
dblp:conf/iccv/JooWZ13
fatcat:uxnf33mdx5eqbdiozdnjv3pd6u
The Multi-level Learning and Classification of Multi-class Parts-Based Representations of U.S. Marine Postures
[chapter]
2009
Lecture Notes in Computer Science
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This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises ...
The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric ...
In the former, parts-based representations are learned for the sole purpose of object detection of that object type. There is no learning of parts that are discriminant between other object types. ...
doi:10.1007/978-3-642-10268-4_59
fatcat:5o4ntduu3zfldowte6ri7ncv6u
Learning coarse-to-fine sparselets for efficient object detection and scene classification
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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More recently the "sparselets" work [1] [2] [3] were introduced to serve as a universal set of shared basis learned from a large number of part detectors, resulting in notable speedup. ...
In order to adequately explore the discriminative information hidden in the part detectors and to achieve sparsity, we propose to optimize a new discriminative objective function by imposing L0-norm sparsity ...
[15] proposed a discriminative variant of mean-shift algorithm for finding mid-level visual elements, which learned 200 the most frequently-occurring elements per class, for a total of 13,400 part detectors ...
doi:10.1109/cvpr.2015.7298721
dblp:conf/cvpr/ChengH0L15
fatcat:ia2fprq4nbbsjlliqge26wzbgu
Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search
[chapter]
2004
Lecture Notes in Computer Science
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That is, given a novel image we want to recognize and localize unseen-before objects based on their similarity to a learned object category. ...
The goal of our work is object categorization in real-world scenes. ...
Acknowledgments: This work is part of the CogVis project, funded by the Comission of the EU (IST-2000-29375) and the Swiss Federal Office for Education and Science (BBW 00.0617). ...
doi:10.1007/978-3-540-28649-3_18
fatcat:6tsr3l63cfawzirinuphtgumuu
When was that made?
[article]
2016
arXiv
pre-print
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In this paper, we explore deep learning methods for estimating when objects were made. ...
We also provide several analyses of what our networks have learned, and demonstrate applications to identifying temporal inspiration in fashion collections. ...
parts of actions [26] , cities [6] , or objects [21] . ...
arXiv:1608.03914v1
fatcat:rihyzdatjbffng6h7uybmlntia
Back to the Future: Learning Shape Models from 3D CAD Data
2010
Procedings of the British Machine Vision Conference 2010
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In this paper, we go back to the ideas from the early days of computer vision, by using 3D object models as the only source of information for building a multi-view object class detector. ...
In particular, we use these models for learning 2D shape that can be robustly matched to 2D natural images. ...
This work has been funded, in part, by the DFG Emmy Noether grant GO1752/3-1. ...
doi:10.5244/c.24.106
dblp:conf/bmvc/StarkGS10
fatcat:scsrtma62rhcnlcu3mpo6h4rzm
Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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We propose a novel approach to boost the performance of generic object detectors on videos by learning videospecific features using a deep neural network. ...
The insight behind our proposed approach is that an object appearing in different frames of a video clip should share similar features, which can be learned to build better detectors. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
doi:10.1109/cvpr.2013.216
dblp:conf/cvpr/YangSS13
fatcat:nuyo7xh7w5fxtpubfak4r5zrcm
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