14.5.4 Learning Object Descriptions, Object Recognition

Cohen, B.[Brian], Sammut, C.[Claude],
Object recognition and concept learning with CONFUCIUS,
PR(15), No. 4, 1982, pp. 309-316.
Elsevier DOI 0309
BibRef

Soucke, B.[Branko],
Fast Learning and Invariant Object Recognition: The Sixth Generation Breakthrough,
New York: Wiley-Interscience1992. ISBN 0-471-57430-9. BibRef 9200

Chan, T.Y.T.[Tony Y.T.], Goldfarb, L.[Lev],
Primitive pattern learning,
PR(25), No. 8, August 1992, pp. 883-889.
Elsevier DOI 0401
BibRef

Edelman, S.,
On Learning to Recognize 3-D Objects from Examples,
PAMI(15), No. 8, August 1993, pp. 833-837.
IEEE DOI BibRef 9308

Edelman, S.,
Representation of Similarity in 3-Dimensional Object Discrimination,
NeurComp(7), No. 2, March 1995, pp. 408-423. BibRef 9503

Edelman, S.[Shimon],
Representing 3D Objects by Sets of Activities of Receptive Fields,
BioCyber(70), 1993, pp. 37-45. BibRef 9300

Edelman, S.[Shimon],
Viewpoint-specific Representations in Three-dimensional Object Recognition,
MIT AI Memo-1239, August 1990. BibRef 9008

Edelman, S.,
Class Similarity and Viewpoint Invariance in the Recognition of 3D Objects,
BioCyber(72), No. 3, February 1995, pp. 207-220. BibRef 9502

Edelman, S.[Shimon], and Weinshall, D.[Daphna],
A Self-Organizing Multiple-View Representation of 3D objects,
BioCyber(64), 1991, pp. 209-219. BibRef 9100

Edelman, S.[Shimon], Bulthoff, H.H.[Heinrich H.],
Orientation Dependence in the Recognition of Familiar and Novel Views of 3D Objects,
Vision Research(32), 1992, pp. 2385-2400. BibRef 9200

Cutzu, F.[Florin], Edelman, S.[Shimon],
Canonical Views in Object Representation and Recognition,
Vision Research(34), 1994, pp. 3037-3056. BibRef 9400

Poggio, T.[Tomaso], and Sung, K.K.[Kah-Kay],
Networks that Learn for Image Understanding,
AIU96(226-240). BibRef 9600

Sung, K.K.[Kah-Kay],
Learning and Example Selection for Object and Pattern Detection,
MIT AI-TR-1572, January 1996.
WWW Link. BibRef 9601

Poggio, T., Edelman, S.,
A Network that Learns to Recognize 3D Objects,
Nature(343), No. 6255, 1990, pp. 263-266. BibRef 9000

Edelman, S., Poggio, T.,
Representations in High-Level Vision: Reassessing the Inverse Optics Paradigm,
DARPA89(944-949). BibRef 8900

Maloof, M.A., Michalski, R.S.,
Learning Symbolic Descriptions of Shape for Object Recognition in X-Ray Images,
ExSysApp(12), No. 1, 1997, pp. 11-20. 9701
BibRef

Lee, C.M., Pong, T.C., Esterline, A., Slagle, J.R.,
KOR: A Knowledge-Based Object Recognition System,
CVIP92(329-362). BibRef 9200

Lee, C.M.[Chung-Mong], Pong, T.C.[Ting-Chuen], Slagle, J.R.[James R.], Esterline, A.[Albert],
An Experimental-Study of an Object Recognition System That Learns,
PR(27), No. 1, January 1994, pp. 65-89.
Elsevier DOI BibRef 9401

Epstein, R., Yuille, A.L., Belhumeur, P.N.,
Learning Object Representations from Lighting Variations,
ORCV96(179) 9611
BibRef

Sugihara, K.[Kokichi],
A graph-theoretical method for monitoring concept formation,
PR(28), No. 11, November 1995, pp. 1635-1643.
Elsevier DOI 0401
Human learning of concepts. BibRef

Wallis, G., Baddeley, R.,
Optimal, Unsupervised Learning in Invariant Object Recognition,
NeurComp(9), No. 4, May 15 1997, pp. 883-894. 9706
BibRef

Peng, J.[Jing], and Bhanu, B.[Bir],
Close-Loop Object Recognition Using Reinforcement Learning,
PAMI(20), No. 2, February 1998, pp. 139-154.
IEEE DOI 9803
BibRef
Earlier: CVPR96(538-543).
IEEE DOI BibRef
And: A2, A1: ARPA94(I:777-780). BibRef

Peng, J.[Jing], Bhanu, B.[Bir],
Local discriminative learning for pattern recognition,
PR(34), No. 1, January 2001, pp. 139-150.
Elsevier DOI 0010
BibRef
And:
Local Reinforcement Learning for Object Recognition,
ICPR98(Vol I: 272-274).
IEEE DOI 9808
BibRef
Earlier:
Delayed Reinforcement Learning for Closed-Loop Object Recognition,
ICPR96(IV: 310-314).
IEEE DOI 9608
BibRef
And: ARPA96(1429-1436). (Univ. of California, Riverside, USA) BibRef

Pauli, J.[Josef],
Learning to Recognize and Grasp Objects,
MachLearn(31), No. 1-3, Apr-Jun 1998, pp. 239-258. 9809
BibRef
Earlier:
Learning Operators for View-Independent Object Recognition,
BMVC96(Poster Session 1). 9608
Christian-Albrechts-Universitat, Germany BibRef

Kervrann, C.[Charles],
Learning probabilistic deformation models from image sequences,
SP(71), No. 2, 15 December 1998, pp. 155-171. BibRef 9812

Newman, R.A.[Rhys A.],
A New Model of Computation for Learning Vision Modules from Examples,
JMIV(11), No. 1, September 1999, pp. 45-63.
DOI Link BibRef 9909

Newman, R.A.[Rhys A.],
Madura: A Language for Learning Vision Programs from Examples,
JMIV(11), No. 1, September 1999, pp. 65-90.
DOI Link BibRef 9909

Pittore, M.[Massimiliano], Campani, M.[Marco], Verri, A.[Alessandro],
Learning to Recognize Visual Dynamic Events from Examples,
IJCV(38), No. 1, June 2000, pp. 35-44.
DOI Link 0006
BibRef

Baldoni, M.[Matteo], Baroglio, C.[Cristina], Cavagnino, D.[Davide],
Use of IFS Codes for Learning 2D Isolated-Object Classification Systems,
CVIU(77), No. 3, March 2000, pp. 371-387.
DOI Link 0004
BibRef

Guo, C.E.[Cheng-En], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Modeling Visual Patterns by Integrating Descriptive and Generative Methods,
IJCV(53), No. 1, June 2003, pp. 5-29.
DOI Link 0304
BibRef
Earlier:
Visual Learning by Integrating Descriptive and Generative Methods,
ICCV01(I: 370-377).
IEEE DOI 0106
BibRef

Xu, Y., Duygulu, P., Saber, E., Tekalp, A.M., Yarman-Vural, F.T.,
Object-based image labeling through learning by example and multi-level segmentation,
PR(36), No. 6, June 2003, pp. 1407-1423.
Elsevier DOI 0304

See also Object Formation by Learning in Visual Databases using Hierarchical Content Description. BibRef

Xu, Y.W.[Yao-Wu], Saber, E.[Eli], Tekalp, A.M.[A. Murat],
Object segmentation and labeling by learning from examples,
IP(12), No. 6, June 2003, pp. 627-638.
IEEE DOI 0307
BibRef

Xu, Y.W.[Yao-Wu], Saber, E.[Eli], Tekalp, A.M.[A. Murat],
Dynamic learning from multiple examples for semantic object segmentation and search,
CVIU(95), No. 3, September 2004, pp. 334-353.
Elsevier DOI 0409
BibRef

Agarwal, S.[Shivani], Awan, A.[Aatif], and Roth, D.[Dan],
Learning to Detect Objects in Images via a Sparse, Part-Based Representation,
PAMI(26), No. 11, November 2004, pp. 1475-1490.
IEEE Abstract. Or:
PDF File.
WWW Link. 0410
Dataset, Vehicles. Detecting specific object classes (e.g. cars). BibRef

Agarwal, S., Roth, D.,
Learning a Sparse Representation for Object Detection,
ECCV02(IV: 113 ff.).
Springer DOI Or:
PDF File. 0205
BibRef

Ferencz, A.[Andras], Learned-Miller, E.G.[Erik G.], Malik, J.[Jitendra],
Learning to Locate Informative Features for Visual Identification,
IJCV(77), No. 1-3, May 2008, pp. 3-24.
Springer DOI 0803
BibRef
Earlier:
Building a Classification Cascade for Visual Identification from One Example,
ICCV05(I: 286-293).
IEEE DOI 0510
E.g. identify a particular car, given one example of the car. Predict most discrimitive features. BibRef

Weinman, J.J.[Jerod J.], Learned-Miller, E.G.[Erik G.],
Improving Recognition of Novel Input with Similarity,
CVPR06(I: 308-315).
IEEE DOI 0606
BibRef

Jain, V., Ferencz, A.[Andras], Learned-Miller, E.G.[Erik G.],
Discriminative Training of Hyper-feature Models for Object Identification,
BMVC06(I:357).
PDF File. 0609
BibRef

Huang, G.B.[Gary B.], Learned-Miller, E.G.[Erik G.],
Learning class-specific image transformations with higher-order Boltzmann machines,
SMiCV10(25-32).
IEEE DOI 1006
E.g. faces as the first example. BibRef

Vijayanarasimhan, S.[Sudheendra], Jain, P.[Prateek], Grauman, K.[Kristen],
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning,
PAMI(36), No. 2, February 2014, pp. 276-288.
IEEE DOI 1402
BibRef
Earlier:
Far-sighted active learning on a budget for image and video recognition,
CVPR10(3035-3042).
IEEE DOI 1006
data analysis. Select most informative instance first. BibRef

Kovashka, A.[Adriana], Vijayanarasimhan, S.[Sudheendra], Grauman, K.[Kristen],
Actively selecting annotations among objects and attributes,
ICCV11(1403-1410).
IEEE DOI 1201
Select based on which will give the most information (from human labeling). BibRef

Liu, Y.Z.[Ya-Zhuo], Zayas-Castro, J.L.[José L.], Fabri, P.[Peter], Huang, S.[Shuai],
Learning high-dimensional networks with nonlinear interactions by a novel tree-embedded graphical model,
PRL(49), No. 1, 2014, pp. 207-213.
Elsevier DOI 1410
High-dimensional network learning with both linear and nonlinear interactions. BibRef

Li, D.B.[De-Bang], Zhang, J.G.[Jun-Ge], Huang, K.Q.[Kai-Qi],
Universal adversarial perturbations against object detection,
PR(110), 2021, pp. 107584.
Elsevier DOI 2011
Adversarial examples, Object detection, Universal adversarial perturbation BibRef

Zhou, C.S.[Chang-Sheng], Zhang, J.S.[Jiang-She], Liu, J.M.[Jun-Min], Zhang, C.X.[Chun-Xia], Shi, G.[Guang], Hu, J.Y.[Jun-Ying],
Bayesian Transfer Learning for Object Detection in Optical Remote Sensing Images,
GeoRS(58), No. 11, November 2020, pp. 7705-7719.
IEEE DOI 2011
Training, Object detection, Bayes methods, Detectors, Random access memory, Optical imaging, Optical sensors, transfer learning BibRef

Lin, Q.F.[Qi-Feng], Long, C.J.[Cheng-Jiang], Zhao, J.H.[Jian-Hui], Fu, G.[Gang], Yuan, Z.Y.[Zhi-Yong],
DDBN: Dual detection branch network for semantic diversity predictions,
PR(122), 2022, pp. 108315.
Elsevier DOI 2112
Adjacent feature compensation, Dual detection branch network, Diversity enhancement strategy, Object detection BibRef

Cheng, B.[Bei], Li, Z.Z.[Zheng-Zhou], Li, H.[Hui], Ding, Z.Q.[Zhi-Quan], Qin, T.Q.[Tian-Qi],
Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Luo, D.P.[Da-Peng], Lei, S.Y.[Si-Yuan], Guo, P.[Peng], Gao, C.X.[Chang-Xin], Chen, Y.[Ying], Li, J.S.[Jin-Sheng], Wei, L.S.[Long-Sheng],
Learning scene-specific object detectors based on a generative-discriminative model with minimal supervision,
PRL(159), 2022, pp. 108-115.
Elsevier DOI 2206
Object detection, Unsupervised learning, Generative-Discriminative model BibRef

Chen, Y.K.[Yu-Kang], Zhang, P.Z.[Pei-Zhen], Kong, T.[Tao], Li, Y.W.[Yan-Wei], Zhang, X.Y.[Xiang-Yu], Qi, L.[Lu], Sun, J.[Jian], Jia, J.Y.[Jia-Ya],
Scale-Aware Automatic Augmentations for Object Detection With Dynamic Training,
PAMI(45), No. 2, February 2023, pp. 2367-2383.
IEEE DOI 2301
Training, Object detection, Task analysis, Adaptation models, Image color analysis, Detectors, Optimization, Scale-aware, dynamic training BibRef

Chen, Y.K.[Yu-Kang], Li, Y.W.[Yan-Wei], Kong, T.[Tao], Qi, L.[Lu], Chu, R.H.[Rui-Hang], Li, L.[Lei], Jia, J.Y.[Jia-Ya],
Scale-aware Automatic Augmentation for Object Detection,
CVPR21(9558-9567)
IEEE DOI 2111
Training, Costs, Estimation, Object detection, Detectors, Search problems BibRef

Peng, C.[Can], Zhao, K.[Kun], Maksoud, S.[Sam], Wang, T.[Tianren], Lovell, B.C.[Brian C.],
DIODE: Dilatable Incremental Object Detection,
PR(136), 2023, pp. 109244.
Elsevier DOI 2301
Incremental learning, Object detection BibRef

Wu, Z.H.[Zhi-Hao], Wen, J.[Jie], Xu, Y.[Yong], Yang, J.[Jian], Zhang, D.[David],
Multiple Instance Detection Networks With Adaptive Instance Refinement,
MultMed(25), 2023, pp. 267-279.
IEEE DOI 2301
Proposals, Training, Annotations, Object detection, Adaptive systems, Benchmark testing, Detectors, Weakly supervised object detection, proposal score BibRef

Li, S.B.[Shi-Bao], Jia, Z.[Zekun], Liu, Y.X.[Yi-Xuan], Cui, X.R.[Xue-Rong], Liu, J.H.[Jian-Hang], Huang, T.P.[Ting-Pei], Xu, J.[Jiuyun],
CLS-DETR: A DETR-series object detection network using classification information to accelerate convergence,
PRL(165), 2023, pp. 168-175.
Elsevier DOI 2301
Artificial intelligence, Deep learning, Computer vision, Object detection BibRef

Yang, K.Q.[Ke-Quan], Li, J.D.[Ji-De], Dai, S.M.[Song-Min], Li, X.Q.[Xiao-Qiang],
Multiscale features integration based multiple-in-single-out network for object detection,
IVC(135), 2023, pp. 104714.
Elsevier DOI 2306
Object detection, Objects of different scales, Single-level feature map, Receptive fields BibRef

Costa, D.[Dinis], Silva, C.[Catarina], Costa, J.[Joana], Ribeiro, B.[Bernardete],
Optimizing Object Detection Models via Active Learning,
IbPRIA23(82-93).
Springer DOI 2307
BibRef

Li, Y.[Yang], Fang, Y.Q.[Yu-Qiang], Li, W.[Wanyun], Jiang, B.[Bitao], Wang, S.J.[Sheng-Jin], Li, Z.[Zhi],
Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images,
RS(15), No. 16, 2023, pp. 3997.
DOI Link 2309
BibRef

Yoon, J.[Jihun], Hong, S.[Seungbum], Choi, M.K.[Min-Kook],
Semi-Supervised Object Detection With Sparsely Annotated Dataset,
ICIP21(719-723)
IEEE DOI 2201
Training, Degradation, Annotations, Statistical analysis, Image processing, Object detection, Detectors, Object detection, sparse annotation BibRef

Hafeez, M.A.[Muhammad Abdullah], Ul-Hasan, A.[Adnan], Shafait, F.[Faisal],
Incremental Learning of Object Detector with Limited Training Data,
DICTA21(01-08)
IEEE DOI 2201
Deep learning, Training, Knowledge engineering, Philosophical considerations, Digital images, Transfer learning, Transfer Learning BibRef

Resnick, C.[Cinjon], Litany, O.[Or], Kar, A.[Amlan], Kreis, K.[Karsten], Lucas, J.[James], Cho, K.[Kyunghyun], Fidler, S.[Sanja],
Causal BERT: Improving object detection by searching for challenging groups,
AVVision21(2972-2981)
IEEE DOI 2112
Training, Neural networks, Object detection, Search problems, Robustness BibRef

Wang, C.Y.[Chien-Yao], Bochkovskiy, A.[Alexey], Liao, H.Y.M.[Hong-Yuan Mark],
Scaled-YOLOv4: Scaling Cross Stage Partial Network,
CVPR21(13024-13033)
IEEE DOI 2111
Computational modeling, Neural networks, Object detection, Pattern recognition BibRef

Chen, Q.[Qiang], Wang, Y.M.[Ying-Ming], Yang, T.[Tong], Zhang, X.Y.[Xiang-Yu], Cheng, J.[Jian], Sun, J.[Jian],
You Only Look One-level Feature,
CVPR21(13034-13043)
IEEE DOI 2111
Training, Memory management, Detectors, Object detection, Feature extraction, Transformers, Solids BibRef

Chen, X.N.[Xiang-Ning], Xie, C.H.[Ci-Hang], Tan, M.X.[Ming-Xing], Zhang, L.[Li], Hsieh, C.J.[Cho-Jui], Gong, B.Q.[Bo-Qing],
Robust and Accurate Object Detection via Adversarial Learning,
CVPR21(16617-16626)
IEEE DOI 2111
Training, Location awareness, Detectors, Object detection, Performance gain, Distortion, Search problems BibRef

Yang, Q.Z.[Qi-Ze], Wei, X.H.[Xi-Han], Wang, B.[Biao], Hua, X.S.[Xian-Sheng], Zhang, L.[Lei],
Interactive Self-Training with Mean Teachers for Semi-supervised Object Detection,
CVPR21(5937-5946)
IEEE DOI 2111
Training, Head, Costs, Fuses, Object detection, Predictive models BibRef

Wang, Z.Y.[Zhen-Yu], Li, Y.[Yali], Guo, Y.[Ye], Fang, L.[Lu], Wang, S.J.[Sheng-Jin],
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection,
CVPR21(4566-4575)
IEEE DOI 2111
Training, Learning systems, Uncertainty, Upper bound, Object detection, Detectors BibRef

Zhou, Q.[Qiang], Yu, C.[Chaohui], Wang, Z.B.[Zhi-Bin], Qian, Q.[Qi], Li, H.[Hao],
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework,
CVPR21(4079-4088)
IEEE DOI 2111
Training, Annotations, Supervised learning, Object detection, Detectors, Manuals BibRef

Bu, X.Y.[Xing-Yuan], Peng, J.[Junran], Yan, J.J.[Jun-Jie], Tan, T.N.[Tie-Niu], Zhang, Z.X.[Zhao-Xiang],
GAIA: A Transfer Learning System of Object Detection that Fits Your Needs,
CVPR21(274-283)
IEEE DOI 2111
Transfer learning, Object detection, Natural language processing, Data models, Pattern recognition BibRef

Li, K.D.[Kai-Dong], Wang, N.Y.[Nina Y.], Yang, Y.[Yiju], Wang, G.H.[Guang-Hui],
SGNet: A Super-class Guided Network for Image Classification and Object Detection,
CRV21(127-134)
IEEE DOI 2108
Knowledge engineering, Annotations, Semantics, Object detection, Predictive models, Robots, Image classification, Deep learning, super-class BibRef

Albaba, B.M.[Berat Mert], Ozer, S.[Sedat],
SyNet: An Ensemble Network for Object Detection in UAV Images,
ICPR21(10227-10234)
IEEE DOI 2105
Deep learning, Shape, Object detection, Detectors, Prediction algorithms, Feature extraction, UAV images BibRef

Hao, M.[Miao], Liu, Y.T.[Yi-Tao], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
LabelEnc: A New Intermediate Supervision Method for Object Detection,
ECCV20(XXV:529-545).
Springer DOI 2011
BibRef

Cheng, M.[Miao], Su, J.P.[Jin-Peng], Li, L.Y.[Lu-Yi], Zhou, X.M.[Xiang-Ming],
A-DFPN: Adversarial Learning and Deformation Feature Pyramid Networks for Object Detection,
ICIVC20(11-18)
IEEE DOI 2009
Feature extraction, Object detection, Detectors, Convolution, Semantics, Strain, Deconvolution, Deformation feature pyramid, adversarial learning BibRef

Ma, L.[Lin], Lu, Z.D.[Zheng-Dong], Shang, L.F.[Li-Feng], Li, H.[Hang],
Multimodal Convolutional Neural Networks for Matching Image and Sentence,
ICCV15(2623-2631)
IEEE DOI 1602
match image and text BibRef

Mao, J.H.[Jun-Hua], Wei, X.[Xu], Yang, Y.[Yi], Wang, J.[Jiang], Huang, Z.H.[Zhi-Heng], Yuille, A.L.[Alan L.],
Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images,
ICCV15(2533-2541)
IEEE DOI 1602
Adaptation models. Learning novel concepts. BibRef

Weng, J.Y.[Ju-Yang], Luciw, M.[Matthew],
Online learning for attention, recognition, and tracking by a single developmental framework,
OLCV10(7-14).
IEEE DOI 1006
Self-Aware and Self-Effecting. Human learning model. BibRef

Kveton, B.[Branislav], Valko, M.[Michal],
Learning from a single labeled face and a stream of unlabeled data,
FG13(1-8)
IEEE DOI 1309
face recognition BibRef

Kveton, B.[Branislav], Philipose, M.[Matthai], Valko, M.[Michal], Huang, L.[Ling],
Online semi-supervised perception: Real-time learning without explicit feedback,
OLCV10(15-21).
IEEE DOI 1006
BibRef

Li, Y.N.[Yuan-Ning], Wang, W.Q.[Wei-Qiang], Gao, W.[Wen],
Object Recognition Based on Dependent Pachinko Allocation Model,
ICIP07(V: 337-340).
IEEE DOI 0709
Pachinko Allocation Model: Learning using graph structures. (From the learning community) BibRef

Wu, Y.[Yang], Yuan, Z.J.[Ze-Jian], Liu, Y.L.[Yuan-Liu], Zheng, N.N.[Nan-Ning],
Discriminative structured outputs prediction model and its efficient online learning algorithm,
Emergent09(2087-2094).
IEEE DOI 0910
Deal with the explosion of data and requirement for detailed analysis of scenes. Not just class, but structured labelling. BibRef

Wu, Y.[Yang], Zheng, N.N.[Nan-Ning], You, Q.B.[Qu-Bo], Du, S.Y.[Shao-Yi],
Object Recognition by Learning Informative, Biologically Inspired Visual Features,
ICIP07(I: 181-184).
IEEE DOI 0709
BibRef

Xu, W.J.[Wen-Jie], Wu, J.K.[Jian-Kang], Huang, Z.Y.[Zhi-Yong],
A maximum margin discriminative learning algorithm for temporal signals,
ICPR06(II: 460-463).
IEEE DOI 0609
BibRef

Forssen, P.E.[Per-Erik], Moe, A.[Anders],
Autonomous Learning of Object Appearances using Colour Contour Frames,
CRV06(3-3).
IEEE DOI 0607
Texture patch model. BibRef

Jojic, N.[Nebojsa], Winn, J.[John], Zitnick, L.[Larry],
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video,
CVPR06(I: 117-124).
IEEE DOI 0606
In learning models the main structure comes out early, fine detail is later. BibRef

Cai, X.C.[Xiong-Cai], Sowmya, A.[Arcot], Trinder, J.[John],
Learning Parameter Tuning for Object Extraction,
ACCV06(I:868-877).
Springer DOI 0601
BibRef

Hadsell, R.[Raia], Chopra, S.[Sumit], Le Cun, Y.L.[Yann L.],
Dimensionality Reduction by Learning an Invariant Mapping,
CVPR06(II: 1735-1742).
IEEE DOI 0606
BibRef

Chopra, S.[Sumit], Hadsell, R.[Raia], Le Cun, Y.L.[Yann L.],
Learning a Similarity Metric Discriminatively, with Application to Face Verification,
CVPR05(I: 539-546).
IEEE DOI 0507
BibRef

Edelman, S.[Shimon], Intrator, N.[Nathan], Jacobson, J.S.[Judah S.],
Unsupervised Learning of Visual Structure,
BMCV02(629 ff.).
Springer DOI 0303
BibRef

Loos, H.S.[Hartmut S.], von der Malsburg, C.[Christoph],
1-Click Learning of Object Models for Recognition,
BMCV02(377 ff.).
Springer DOI 0303
BibRef

Lömker, F., Sagerer, G.,
A Multimodal System for Object Learning,
DAGM02(490 ff.).
Springer DOI 0303
BibRef

Lashkia, G.V.,
Learning with relevant features and examples,
ICPR02(II: 68-71).
IEEE DOI 0211
BibRef

Roobaert, D.[Danny], Zillich, M.[Michael], Eklundh, J.O.[Jan-Olof],
A Pure Learning Approach to Background-Invariant Object Recognition Using Pedagogical Support Vector Learning,
CVPR01(II:351-357).
IEEE DOI 0110
BibRef

Caelli, T.M.,
Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance,
ICPR00(Vol II: 215-218).
IEEE DOI 0009
BibRef

Duta, N.[Nicolae], Jain, A.K.[Anil K.], Dubuisson-Jolly, M.P.[Marie-Pierre],
Learning-based Object Detection in Cardiac MR Images,
ICCV99(1210-1216).
IEEE DOI BibRef 9900

Duta, N.[Nicolae], Jain, A.K.[Anil K.], Dubuisson-Jolly, M.P.[Marie-Pierre],
Learning 2D Shape Models,
CVPR99(II: 8-14).
IEEE DOI Clustering training shapes. BibRef 9900

Kato, T., Ninomiya, Y.,
An Approach to Vehicle Recognition Using Supervised Learning,
MVA98(xx-yy). BibRef 9800

Vetter, T.[Thomas], Jones, M.J.[Michael J.], Poggio, T.[Tomaso],
A Bootstrapping Algorithm for Learning Linear Models of Object Classes,
CVPR97(40-46).
IEEE DOI 9704
BibRef
And: DARPA97(1373-1378). Faces and digits. BibRef

Shu, D.B.[David B.], and Li, C.C.,
Reordering of Surface Feature Vectors in Training for 3-D Object Recognition,
ICPR86(111-115). BibRef 8600

Tomita, F.,
A Learning Vision System for 2D Object Recognition,
IJCAI83(1132-1135). BibRef 8300

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Self-Supervised Learning .