13.6.11 Context in Computer Vision

Strat, T.M., and Fischler, M.A.,
Context-Based Vision: Recognizing Objects Using Information from Both 2-D and 3-D Imagery,
PAMI(13), No. 10, October 1991, pp. 1050-1065.
IEEE DOI System: Condor. BibRef 9110
Earlier:
A Context-Based Recognition System for Natural Scenes and Complex Domains,
DARPA90(456-472). BibRef
Earlier: A2, A1:
Recognizing Objects in a Natural Environment: A Contextual Vision System,
DARPA89(774-796). BibRef
And:
Context-Based Vision: Recognition of Natural Scenes,
Asilomar89(532-536). System: CVS. Recognition, Context Based. This discusses the current SRI high-level vision effort. Addresses: object recognition without accurate object delineation, use of contest, use of geometry, and control of complexity. Uses context sets and cliques. BibRef

Strat, T.M.,
Decision Analysis Using Belief Functions,
ApproximateR(4), No. 5, September, 1990, pp. 391-418. BibRef 9009
And: in Advances in the Dempster-Shafer Theory of Evidence, ed. by R. Yager, M. Fedrizzi, and J. Kacprzyk, John Wiley & sons, New York, 1994, pp. 275-310.
See also Mathematical Theory of Evidence, A. BibRef

Strat, T.M.,
Explaining Evidential Analyses,
ApproximateR(3), No. 4, July 1989, pp. 299-353. BibRef 8907

Strat, T.M.,
Natural Object Recognition,
New York: Springer1992, 165pp. ISBN 0-387-97832-1. BibRef 9200
And: STAN-CS-91-1376, Stanford, CA, December 1990. BibRef Ph.D.Thesis. System: Condor. Rule Based Analysis. The BibRef Bookfrom his thesis on general object recognition using contextual cues. A set of processes interact through shared data structures. Each process has an associated context set, that when satisfied causes the process to run. BibRef

Strat, T.M.,
Photogrammetry and Knowledge Representation in Computer Vision,
ISPRS94Symposium on Spatial Information from Digital Photogrammetry and Computer Vision, Munich, September 1994. BibRef 9409

Strat, T.M., Fischler, M.A.,
Natural Object Recognition: A Theoretical Framework and Its Implementation,
IJCAI91(1264-1270). BibRef 9100

Strat, T.M.[Thomas M.], Fischler, M.A.[Martin A.],
The Use of Context in Vision,
Context95(xx) BibRef 9500

Strat, T.M.[Thomas M.], Fua, P.V., Connolly, C.I.,
Context-Based Vision,
Radius97(373-388). BibRef 9700

Smith, G.B., Strat, T.M.,
Information Management in a Sensor-Based Autonomous System,
DARPA87(170-177). BibRef 8700

Strat, T.M., Smith, G.B.,
Core Knowledge System: Storage and Retrieval of Inconsistent Information,
DARPA88(660-665). BibRef 8800

Strat, T.M.,
Using Context to Control Computer Vision Algorithms,
Ascona95(3-12). BibRef 9500
Earlier:
Employing Contextual Information in Computer Vision,
DARPA93(217-229). System: Condor. The use of context in understanding objects. Describes the Prolog-like language used to control algorithms in RCDE BibRef

Strat, T.M., Smith, G.B.,
The Management of Spatial Information in a Mobile Robot,
SRMSF87(240-249). BibRef 8700

Lowrance, J., Strat, T.M., Wesley, L.P., Garvey, T.D., and Ruspini, E.,
The Theory, Implementation, and Practice of Evidential Reasoning,
Final Report, SRIProject 5701, June 1991. BibRef 9106

Lowrance, J., Ruspini, E., and Strat, T.M.,
Understanding Evidential Reasoning,
SRI-TN-501, January 1991. BibRef 9101

Bell, B., Pau, L.F.,
Context Knowledge and Search Control Issues in Object-Oriented Prolog-Based Image Understanding,
PRL(13), 1992, pp. 279-290. BibRef 9200

Pau, L.F.,
Context Related Issues in Image Understanding,
HPRCV97(Chapter IV:3). (Digital Equipment Europe) BibRef 9700

Liedtke, C.E., Bückner, J., Pahl, M., Stahlhut, O.,
Knowledge Based System for the Interpretation of Complex Scenes,
Ascona01(3-12). Combine various features for roads and buildings. 0201
BibRef

Bückner, J., Pahl, M., Stahlhut, O., Liedtke, C.E.,
A Knowledge-Based System for Context Dependent Evaluation of Remote Sensing Data,
DAGM02(58 ff.).
Springer DOI 0303
BibRef

Bückner, J.[Jürgen], Pahl, M.[Martin], Stahlhut, O.[Oliver],
Semantic Interpretation of Remote Sensing Data,
PCV02(A: 62). 0305
BibRef

Vailaya, A.[Aditya], Zhang, H.J.[Hong-Jiang], Yang, C.J.[Chang-Jiang], Liu, F.I.[Feng-I], Jain, A.K.[Anil K.],
Automatic image orientation detection,
IP(11), No. 7, July 2002, pp. 746-755.
IEEE DOI 0207
BibRef
Earlier: A1, A2, A5 Only: ICIP99(II:600-604).
IEEE DOI Find the orientation of the natural image using features. BibRef

Zhou, G.D.[Guo-Dong],
Direct modelling of output context dependence in discriminative hidden Markov model,
PRL(26), No. 5, April 2005, pp. 545-553.
Elsevier DOI 0501
BibRef

Luo, J.B.[Jie-Bo], Boutell, M.R.[Matthew R.],
Automatic Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues,
PAMI(27), No. 5, May 2005, pp. 715-726.
IEEE Abstract. 0501
BibRef
Earlier:
A Probabilistic Approach to Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues,
MMDE04(141).
IEEE DOI 0406
How to display random collections of (consumer) images in the correct orientation. Low level cues are not sufficient. BibRef

Boutell, M.R.[Matthew R.], Luo, J.B.[Jie-Bo],
Bayesian fusion of camera metadata cues in semantic scene classification,
CVPR04(II: 623-630).
IEEE DOI 0408
Use camera metadata to aid classification (e.g. exposure time). BibRef

Wolf, L.B.[Lior B.], Bileschi, S.M.[Stanley M.],
A Critical View of Context,
IJCV(69), No. 2, August 2006, pp. 251-261.
Springer DOI 0606
Use context to select locations likely to contain particular objects. BibRef

Wolf, L.B.[Lior B.], Bileschi, S.M.[Stan M.], Meyers, E.[Ethan],
Perception Strategies in Hierarchical Vision Systems,
CVPR06(II: 2153-2160).
IEEE DOI 0606
BibRef

Bileschi, S.M.[Stanley M.],
StreetScenes: Towards Scene Understanding in Still Images,
Ph.D.Thesis, May 2006, MIT.
PDF File. BibRef 0605

Bileschi, S.M.[Stanley M.],
CBCL StreetScenes Challenge Framework,
Online2007.
WWW Link. Dataset, Object Detection. Primarily for Cars, people, and street scenes. Data is labeled. BibRef 0700

Hoiem, D.[Derek], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
Recovering Surface Layout from an Image,
IJCV(75), No. 1, October 2007, pp. 151-172.
Springer DOI 0709
BibRef
Earlier:
Geometric Context from a Single Image,
ICCV05(I: 654-661).
IEEE DOI 0510
Dataset, Recognition. The example data is available:
HTML Version. Kanade issue. Coarse properties (ground plane, sky, planar regions) from one image. Probabilistic approach to estimate 3D geometry so that not every possible view is needed. BibRef

Quickly determine the approximate surface structure from variety of cues.

Hoiem, D.[Derek], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
Closing the loop in scene interpretation,
CVPR08(1-8).
IEEE DOI
PDF File. 0806
Combining the IU tasks. BibRef

Efros, A.A.[Alexei Alyosha],
Qualitative 3D from a Single Image,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef

Hoiem, D.[Derek], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
Putting Objects in Perspective,
IJCV(80), No. 1, October 2008, pp. xx-yy.
Springer DOI
PDF File. 0809
BibRef
Earlier: CVPR06(II: 2137-2144).
IEEE DOI
HTML Version. 0606
Award, CVPR. BibRef

Divvala, S.K.[Santosh Kumar], Efros, A.A.[Alexei A.], Hebert, M.[Martial], Lazebnik, S.[Svetlana],
Unsupervised Patch-based Context from Millions of Images,
CMU-RI-TR-11-38, December, 2011.
PDF File. 1202
BibRef

Divvala, S.K.[Santosh K.], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
Object Instance Sharing by Enhanced Bounding Box Correspondence,
BMVC12(60).
DOI Link 1301
BibRef

Divvala, S.K.[Santosh K.], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
Can similar scenes help surface layout estimation?,
InterNet08(1-8).
IEEE DOI 0806
BibRef

Hoiem, D.[Derek],
Seeing the World Behind the Image: Spatial Layout for 3D Scene Understanding,
CMU-RI-TR-07-28, August, 2007. BibRef 0708 Ph.D.Thesis.
WWW Link. BibRef

Hoiem, D.[Derek], Rother, C.[Carsten], Winn, J.[John],
3D Layout CRF for Multi-View Object Class Recognition and Segmentation,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Bauckhage, C., Wachsmuth, S., Hanheide, M., Wrede, S., Sagerer, G.F., Heidemann, G., Ritter, H.,
The visual active memory perspective on integrated recognition systems,
IVC(26), No. 1, 1 January 2008, pp. 5-14.
Elsevier DOI 0711
Cognitive vision; Contextual reasoning; Fusion; Architecture; System integration BibRef

Carbonetto, P.[Peter], Dorkó, G.[Gyuri], Schmid, C.[Cordelia], Kück, H.[Hendrik], de Freitas, N.[Nando],
Learning to Recognize Objects with Little Supervision,
IJCV(77), No. 1-3, May 2008, pp. 219-237.
Springer DOI 0803
BibRef
Earlier:
A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues,
CLOR06(277-300).
Springer DOI 0711
BibRef

Kück, H.[Hendrik], Hoffman, M.[Matt], Doucet, A.[Arnaud], de Freitas, N.[Nando],
Inference and Learning for Active Sensing, Experimental Design and Control,
IbPRIA09(1-10).
Springer DOI 0906
BibRef

Carbonetto, P.[Peter], de Freitas, N.[Nando], Barnard, K.[Kobus],
A Statistical Model for General Contextual Object Recognition,
ECCV04(Vol I: 350-362).
Springer DOI 0405
Given the image and captions (or descriptions) learn the spatial relationships. BibRef

Kück, H.[Hendrik], Carbonetto, P.[Peter], de Freitas, N.[Nando],
A Constrained Semi-supervised Learning Approach to Data Association,
ECCV04(Vol III: 1-12).
Springer DOI 0405
Show how a wide class of data association tasks arising in computer vision can be interpreted as a constrained semi-supervised learning problem. BibRef

Fan, J.P., Shen, Y., Yang, C., Zhou, N.,
Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation,
IP(20), No. 3, March 2011, pp. 837-854.
IEEE DOI 1103

See also Generalized feature learning and indexing for object localization and recognition. BibRef

Dong, P.X.[Pei-Xiang], Mei, K.Z.[Kui-Zhi], Zheng, N.N.[Nan-Ning], Lei, H.[Hao], Fan, J.P.[Jian-Ping],
Training inter-related classifiers for automatic image classification and annotation,
PR(46), No. 5, May 2013, pp. 1382-1395.
Elsevier DOI 1302
Inter-related classifier training; Large-scale image classification; Structural learning; Visual concept network BibRef

Lei, H.[Hao], Mei, K.Z.[Kui-Zhi], Zheng, N.N.[Nan-Ning], Dong, P.X.[Pei-Xiang], Zhou, N.[Ning], Fan, J.P.[Jian-Ping],
Learning group-based dictionaries for discriminative image representation,
PR(47), No. 2, 2014, pp. 899-913.
Elsevier DOI 1311
Group-based dictionary learning BibRef

Xue, X.Y.[Xiang-Yang], Luo, H.Z.[Hang-Zai], Fan, J.P.[Jian-Ping],
Structured max-margin learning for multi-label image annotation,
CIVR10(82-88).
DOI Link 1007
Multiple classifiers. BibRef

Zhou, N.[Ning], Shen, Y.[Yi], Peng, J.Y.[Jin-Ye], Fan, J.P.[Jian-Ping],
Learning inter-related visual dictionary for object recognition,
CVPR12(3490-3497).
IEEE DOI 1208
BibRef

Zhou, N.[Ning], Cheung, W.K.[William K.], Qiu, G.P.[Guo-Ping], Xue, X.Y.[Xiang-Yang],
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging,
PAMI(33), No. 7, July 2011, pp. 1281-1294.
IEEE DOI 1106
BibRef
Earlier: A1, A2, A4, A3:
Collaborative and content-based image labeling,
ICPR08(1-4).
IEEE DOI 0812
User data with labels provided. Integrate low-level image features with user provided tags. BibRef

Liu, X.B.[Xiao-Bai], Lin, L.[Liang], Yan, S.C.[Shui-Cheng], Jin, H.[Hai], Tao, W.B.[Wen-Bing],
Integrating Spatio-Temporal Context With Multiview Representation for Object Recognition in Visual Surveillance,
CirSysVideo(21), No. 4, April 2011, pp. 393-407.
IEEE DOI 1104
set of deformable object templates for a pose, temporal context, BibRef

Choi, M.J.[Myung Jin], Torralba, A.B.[Antonio B.], Willsky, A.S.[Alan S.],
A Tree-Based Context Model for Object Recognition,
PAMI(34), No. 2, February 2012, pp. 240-252.
IEEE DOI 1112
BibRef
Earlier: Add A2: Lim, J.J.[Joseph J.], CSAIL(TR-2010-050). 2010-10-29
WWW Link. 1101
Recognition in context in addition to local features. Context rules out unlikely conbinations or locations. Also gets most typical or least typical scene in dataset. BibRef

Choi, M.J.[Myung Jin], Torralba, A.B.[Antonio B.], Willsky, A.S.[Alan S.],
Context models and out-of-context objects,
PRL(33), No. 7, 1 May 2012, pp. 853-862.
Elsevier DOI 1203
Award, IAPR, J.K. Aggarwal. Object detection; Context model; Out-of-context object BibRef

Choi, M.J.[Myung Jin], Lim, J.J.[Joseph J.], Torralba, A.B.[Antonio B.], Willsky, A.S.[Alan S.],
Exploiting hierarchical context on a large database of object categories,
CVPR10(129-136).
IEEE DOI 1006
BibRef

Helmy, T.[Tarek],
A Computational Model for Context-based Image Categorization and Description,
IJIG(12), No. 1, January 2012, pp. 1250001.
DOI Link 1203
BibRef

Parikh, D.[Devi], Zitnick, C.L.[C. Lawrence], Chen, T.H.[Tsu-Han],
Exploring Tiny Images: The Roles of Appearance and Contextual Information for Machine and Human Object Recognition,
PAMI(34), No. 10, October 2012, pp. 1978-1991.
IEEE DOI 1208
BibRef
Earlier:
From appearance to context-based recognition: Dense labeling in small images,
CVPR08(1-8).
IEEE DOI 0806
Context in addition to the object itself. BibRef

Zitnick, C.L.[C. Lawrence], Parikh, D.[Devi],
The role of image understanding in contour detection,
CVPR12(622-629).
IEEE DOI 1208
BibRef
Earlier: A2, A1:
The role of features, algorithms and data in visual recognition,
CVPR10(2328-2335).
IEEE DOI 1006
Analysis of human performance for designing recognition systems. BibRef

Guo, Z.Y.[Zhen-Yu], Wang, Z.J.,
An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition,
MultMed(15), No. 3, 2013, pp. 621-632.
IEEE DOI 1303
BibRef
Earlier:
One-shot Recognition Using Unsupervised Attribute-Learning,
PSIVT10(1-6).
IEEE DOI 1011
High level descriptions to aid recognition. BibRef

Xue, J.R.[Jian-Ru], Wang, L.[Le], Zheng, N.N.[Nan-Ning], Hua, G.[Gang],
Automatic salient object extraction with contextual cue and its applications to recognition and alpha matting,
PR(46), No. 11, November 2013, pp. 2874-2889.
Elsevier DOI 1306
BibRef
Earlier: A2, A1, A3, A4:
Automatic salient object extraction with contextual cue,
ICCV11(105-112).
IEEE DOI 1201
Salient object; Object extraction; Graph cut; Visual attention; Visual context BibRef

Grim, J.[Jirí],
Sequential pattern recognition by maximum conditional informativity,
PRL(45), No. 1, 2014, pp. 39-45.
Elsevier DOI 1407
Multivariate statistics. Choose the next feature to measure. BibRef

Sun, L.J.[Lin-Jia], Liang, X.H.[Xiao-Hui], Zhao, Q.P.[Qin-Ping],
Automatic sub-category partitioning and parts localization for learning a robust object model,
IVC(32), No. 9, 2014, pp. 579-589.
Elsevier DOI 1408
Object model learning from topic model. Multiple versions (categories) for same object. BibRef

Pinz, A.J.[Axel J.],
Object Categorization,
FTCGV(1), Issue 4, 2005, pp. 255-353.
DOI Link 1410
Published September 2006. BibRef

Lin, L., Zhang, R., Duan, X.,
Adaptive Scene Category Discovery With Generative Learning and Compositional Sampling,
CirSysVideo(25), No. 2, February 2015, pp. 251-260.
IEEE DOI 1502
Clustering algorithms BibRef

Cao, X., Wei, X., Han, Y., Chen, X.,
An Object-Level High-Order Contextual Descriptor Based on Semantic, Spatial, and Scale Cues,
Cyber(45), No. 7, July 2015, pp. 1327-1339.
IEEE DOI 1506
Context BibRef

Lang, H., Ling, H.,
Covert Photo Classification by Fusing Image Features and Visual Attributes,
IP(24), No. 10, October 2015, pp. 2996-3008.
IEEE DOI 1507
Cameras. I.e. which photos were taken without the subject's awareness. BibRef

Zhang, X.S.[Xi-Shan], Yang, Y.[Yang], Zhang, Y.D.[Yong-Dong], Luan, H.B.[Huan-Bo], Li, J.T.[Jin-Tao], Zhang, H.W.[Han-Wang], Chua, T.S.[Tat-Seng],
Enhancing Video Event Recognition Using Automatically Constructed Semantic-Visual Knowledge Base,
MultMed(17), No. 9, September 2015, pp. 1562-1575.
IEEE DOI 1509
image classification BibRef

Zhang, X.S.[Xi-Shan], Zhang, H.W.[Han-Wang], Zhang, Y.D.[Yong-Dong], Yang, Y.[Yang], Wang, M.[Meng], Luan, H.B.[Huan-Bo], Li, J.T.[Jin-Tao], Chua, T.S.[Tat-Seng],
Deep Fusion of Multiple Semantic Cues for Complex Event Recognition,
IP(25), No. 3, March 2016, pp. 1033-1046.
IEEE DOI 1602
feature extraction BibRef

Ohn-Bar, E.[Eshed], Trivedi, M.M.[Mohan Manubhai],
Multi-scale volumes for deep object detection and localization,
PR(61), No. 1, 2017, pp. 557-572.
Elsevier DOI 1705
BibRef
Earlier:
Detection and localization with multi-scale models,
ICPR16(1382-1387)
IEEE DOI 1705
Analytical models, Automobiles, Computational modeling, Feature extraction, Image resolution, Pipelines, Training. Multi-scale reasoning
See also Learning to Detect Vehicles by Clustering Appearance Patterns. BibRef

Premachandran, V.[Vittal], Tarlow, D.[Daniel], Yuille, A.L., Batra, D.[Dhruv],
Empirical Minimum Bayes Risk Prediction,
PAMI(39), No. 1, January 2017, pp. 75-86.
IEEE DOI 1612
BibRef
Earlier: A1, A2, A4, Only:
Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters,
CVPR14(1043-1050)
IEEE DOI 1409
Decision theory. Maximize for specific performance measure. BibRef

Huang, C.[Chao], Li, H.L.[Hong-Liang], Li, W.[Wei], Wu, Q.B.[Qing-Bo], Xu, L.F.[Lin-Feng],
Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN,
JVCIR(44), No. 1, 2017, pp. 21-28.
Elsevier DOI 1703
Store classification. what kind of store is it. BibRef

Oramas Mogrovejo, J.A.[José Antonio], de Raedt, L.[Luc], Tuytelaars, T.[Tinne],
Context-based object viewpoint estimation: A 2D relational approach,
CVIU(160), No. 1, 2017, pp. 100-113.
Elsevier DOI 1706
BibRef
Earlier:
Allocentric Pose Estimation,
ICCV13(289-296)
IEEE DOI 1403
Context. allocentric; collective; configuration; context; pose; viewpoint Use context of other objects. BibRef

Zhang, C.J.[Chun-Jie], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Contextual Exemplar Classifier-Based Image Representation for Classification,
CirSysVideo(27), No. 8, August 2017, pp. 1691-1699.
IEEE DOI 1708
Computational modeling, Context modeling, Feature extraction, Image representation, Semantics, Training, Visualization, image processing, pattern classification BibRef

Tariq, A.[Amara], Foroosh, H.[Hassan],
Designing a symmetric classifier for image annotation using multi-layer sparse coding,
IVC(69), No. 1, 2018, pp. 33-43.
Elsevier DOI 1712
BibRef
Earlier:
Feature-independent context estimation for automatic image annotation,
CVPR15(1958-1965)
IEEE DOI 1510
BibRef
Earlier:
Scene-based automatic image annotation,
ICIP14(3047-3051)
IEEE DOI 1502
Automatic image annotation. Computational modeling BibRef

Zhuang, Y.T.[Yue-Ting], Song, J.[Jun], Wu, F.[Fei], Li, X.[Xi], Zhang, Z.F.[Zhong-Fei], Rui, Y.[Yong],
Multimodal Deep Embedding via Hierarchical Grounded Compositional Semantics,
CirSysVideo(28), No. 1, January 2018, pp. 76-89.
IEEE DOI 1801
Bicycles, Buildings, Correlation, Feature extraction, Machine learning, Semantics, Visualization, multimodal embedding. BibRef

Owens, A.[Andrew], Wu, J.J.[Jia-Jun], McDermott, J.H.[Josh H.], Freeman, W.T.[William T.], Torralba, A.B.[Antonio B.],
Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning,
IJCV(126), No. 10, October 2018, pp. 1120-1137.
Springer DOI 1809
BibRef
Earlier:
Ambient Sound Provides Supervision for Visual Learning,
ECCV16(I: 801-816).
Springer DOI 1611
Context (ocean, traffic, etc.) BibRef

Zhuang, C.[Can], Xie, Z.[Zhong], Ma, K.[Kai], Guo, M.Q.[Ming-Qiang], Wu, L.[Liang],
A Task-Oriented Knowledge Base for Geospatial Problem-Solving,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Liang, K.M.[Kong-Ming], Chang, H.[Hong], Ma, B., Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Unifying Visual Attribute Learning with Object Recognition in a Multiplicative Framework,
PAMI(41), No. 7, July 2019, pp. 1747-1760.
IEEE DOI 1906
BibRef
Earlier: A1, A2, A4, A5, Only:
A Unified Multiplicative Framework for Attribute Learning,
ICCV15(2506-2514)
IEEE DOI 1602
Visualization, Semantics, Predictive models, Task analysis, Machine learning, Training, Correlation, Attribute learning, image understanding. mid-level semantic properties of objects BibRef

Sun, N., Li, W., Liu, J., Han, G., Wu, C.,
Fusing Object Semantics and Deep Appearance Features for Scene Recognition,
CirSysVideo(29), No. 6, June 2019, pp. 1715-1728.
IEEE DOI 1906
Feature extraction, Semantics, Image recognition, Databases, Visualization, Hidden Markov models, Benchmark testing, scene recognition BibRef

Ji, Q.A.[Qi-Ang],
Combining knowledge with data for efficient and generalizable visual learning,
PRL(124), 2019, pp. 31-38.
Elsevier DOI 1906
Propose to classify knowledge into permanent knowledge, circumstantial knowledge, subjective knowledge, and data knowledge. Machine learning, Object recognition BibRef

Shen, F.M.[Fu-Min], Zhou, X.[Xiang], Yu, J.[Jun], Yang, Y.[Yang], Liu, L.[Li], Shen, H.T.[Heng Tao],
Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings,
IP(28), No. 7, July 2019, pp. 3662-3674.
IEEE DOI 1906
Semantics, Visualization, Task analysis, Binary codes, Training, Image coding, Hamming distance, Zero-shot learning, binary embeddings BibRef

Liu, Y.[Yang], Gao, X.B.[Xin-Bo], Han, J.G.[Jun-Gong], Liu, L.[Li], Shao, L.[Ling],
Zero-shot learning via a specific rank-controlled semantic autoencoder,
PR(122), 2022, pp. 108237.
Elsevier DOI 2112
Zero-shot learning, Rank, Domain shift, Autoencoder BibRef

Yang, J.[Junbo], Hu, B.[Borui], Li, H.Y.[Han-Yu], Liu, Y.[Yang], Gao, X.B.[Xin-Bo], Han, J.G.[Jun-Gong], Chen, F.L.[Fang-Lin], Wu, X.[Xuangou],
Dynamic VAEs via semantic-aligned matching for continual zero-shot learning,
PR(160), 2025, pp. 111199.
Elsevier DOI 2501
Continual zero-shot learning, Catastrophic forgetting, Semantic-aligned matching, Variational autoencoders BibRef

Gao, R.[Rui], Hou, X.S.[Xing-Song], Qin, J.[Jie], Shen, Y.M.[Yu-Ming], Long, Y.[Yang], Liu, L.[Li], Zhang, Z.[Zhao], Shao, L.[Ling],
Visual-Semantic Aligned Bidirectional Network for Zero-Shot Learning,
MultMed(25), 2023, pp. 1649-1664.
IEEE DOI 2306
Semantics, Visualization, Task analysis, Training, Standards, Data models, Computational modeling, Bidirectional network, zero-shot learning BibRef

Long, Y.[Yang], Liu, L.[Li], Shao, L.[Ling],
Towards Fine-Grained Open Zero-Shot Learning: Inferring Unseen Visual Features from Attributes,
WACV17(944-952)
IEEE DOI 1609
BibRef
Earlier:
Attribute Embedding with Visual-Semantic Ambiguity Removal for Zero-shot Learning,
BMVC16(xx-yy).
HTML Version. 1805
Correlation, Gold, Semantics, Support vector machines, Training, Training data, Visualization BibRef

Tang, Y.W.[Yun-Wei], Jing, L.H.[Lin-Hai], Shi, F.[Fan], Li, X.[Xiao], Qiu, F.[Fang],
A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Min, W., Mei, S., Liu, L., Wang, Y., Jiang, S.,
Multi-Task Deep Relative Attribute Learning for Visual Urban Perception,
IP(29), No. 1, 2020, pp. 657-669.
IEEE DOI 1910
Quantify perceptual attributes (e.g., safe and depressing attributes) of physical urban environment from crowd-sourced street-view images. data visualisation, feature extraction, learning (artificial intelligence), regression analysis, multi-task learning BibRef

Yang, S.J.[Shi-Jie], Li, L.[Liang], Wang, S.H.[Shu-Hui], Zhang, W.G.[Wei-Gang], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
SkeletonNet: A Hybrid Network With a Skeleton-Embedding Process for Multi-View Image Representation Learning,
MultMed(21), No. 11, November 2019, pp. 2916-2929.
IEEE DOI 1911
Semantics, Correlation, Visualization, Skeleton, Matrix decomposition, Kernel, Laplace equations, deep auto-encoders BibRef

Ren, C.X.[Chuan-Xian], Luo, Y.W.[You-Wei], Xu, X.L.[Xiao-Lin], Dai, D.Q.[Dao-Qing], Yan, H.[Hong],
Discriminative Residual Analysis for Image Set Classification With Posture and Age Variations,
IP(29), 2020, pp. 2875-2888.
IEEE DOI 2001
Image set -- find the intrinsic connection when images have variations. Image recognition, Feature extraction, Noise measurement, Training, Task analysis, Databases, Image set recognition, residual analysis, regularization BibRef

Peng, H., Zhang, Y., Yang, S., Song, B.,
Battlefield Image Situational Awareness Application Based on Deep Learning,
IEEE_Int_Sys(35), No. 1, January 2020, pp. 36-43.
IEEE DOI 2004
Convolution, Feature extraction, Training data, Deep learning, Object detection, Neural networks, Human computer interaction, Situational awareness BibRef

Liu, J.Y.[Jing-Yu], Wang, W.[Wei], Wang, L.[Liang], Yang, M.H.[Ming-Hsuan],
Attribute-Guided Attention for Referring Expression Generation and Comprehension,
IP(29), 2020, pp. 5244-5258.
IEEE DOI 2004
The goal of referring expression is to refer to a particular object in some scenarios. Task analysis, Visualization, Training, Detectors, Standards, Natural languages, Feature extraction, Referring expression, attribute-guided attention BibRef

Chen, G.W.[Gong-Wei], Song, X.H.[Xin-Hang], Zeng, H.T.[Hai-Tao], Jiang, S.Q.[Shu-Qiang],
Scene Recognition With Prototype-Agnostic Scene Layout,
IP(29), 2020, pp. 5877-5888.
IEEE DOI 2005
Layout, Semantics, Prototypes, Image recognition, Convolution, Neural networks, Deformable models, Scene classification, scene layout BibRef

Zeng, H.T.[Hai-Tao], Song, X.H.[Xin-Hang], Chen, G.W.[Gong-Wei], Jiang, S.Q.[Shu-Qiang],
Learning Scene Attribute for Scene Recognition,
MultMed(22), No. 6, June 2020, pp. 1519-1530.
IEEE DOI 2005
Scene attributes, not object features. Feature extraction, Visualization, Semantics, Context modeling, Image recognition, Aggregates, Scene recognition, scene attribute BibRef

Nguyen, K.[Kien], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Context from within: Hierarchical context modeling for semantic segmentation,
PR(105), 2020, pp. 107358.
Elsevier DOI 2006
Semantic segmentation, Context modeling, Network evolution, Conditional random field, Probabilistic graphical models BibRef

Fernández-Torres, M.Á.[Miguel-Ángel], González-Díaz, I.[Iván], Díaz-de-María, F.[Fernando],
Probabilistic Topic Model for Context-Driven Visual Attention Understanding,
CirSysVideo(30), No. 6, June 2020, pp. 1653-1667.
IEEE DOI 2006
Visualization, Task analysis, Adaptation models, Feature extraction, Computational modeling, Probabilistic logic, latent topic models BibRef

Ärje, J.[Johanna], Raitoharju, J.[Jenni], Iosifidis, A.[Alexandros], Tirronen, V.[Ville], Meissner, K.[Kristian], Gabbouj, M.[Moncef], Kiranyaz, S.[Serkan], Kärkkäinen, S.[Salme],
Human experts vs. machines in taxa recognition,
SP:IC(87), 2020, pp. 115917.
Elsevier DOI 2007
Hierarchical classification, Taxonomy, Convolutional neural networks, Taxonomic expert, Biomonitoring BibRef

Lv, F.M.[Feng-Mao], Lin, G.S.[Guo-Sheng], Liu, P.[Peng], Yang, G.W.[Guo-Wu], Pan, S.J.L.[Sinno Jia-Lin], Duan, L.X.[Li-Xin],
Weakly-Supervised Cross-Domain Road Scene Segmentation via Multi-Level Curriculum Adaptation,
CirSysVideo(31), No. 9, September 2021, pp. 3493-3503.
IEEE DOI 2109
Image segmentation, Semantics, Annotations, Roads, Training, Task analysis, Reliability, Semantic segmentation, weakly-supervised learning BibRef

Fu, L.Y.[Li-Yong], Zhang, D.[Dong], Ye, Q.L.[Qiao-Lin],
Recurrent Thrifty Attention Network for Remote Sensing Scene Recognition,
GeoRS(59), No. 10, October 2021, pp. 8257-8268.
IEEE DOI 2109
Acquire context for remote sensing analysis. Image recognition, Task analysis, Standards, Object detection, Manuals, Forestry, Semantics, Attention learning, RSS recognition BibRef

Ghadi, Y.Y.[Yazeed Yasin], Rafique, A.A.[Adnan Ahmed], al Shloul, T.[Tamara], Alsuhibany, S.A.[Suliman A.], Jalal, A.[Ahmad], Park, J.M.[Jeong-Min],
Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Hao, Y.B.[Yan-Bin], Wang, S.[Shuo], Cao, P.[Pei], Gao, X.J.[Xin-Jian], Xu, T.[Tong], Wu, J.M.[Jin-Meng], He, X.N.[Xiang-Nan],
Attention in Attention: Modeling Context Correlation for Efficient Video Classification,
CirSysVideo(32), No. 10, October 2022, pp. 7120-7132.
IEEE DOI 2210

WWW Link. Context modeling, Computational modeling, Correlation, Visualization, Convolutional neural networks, Standards, efficient calculation BibRef

Yuan, J.W.[Jing-Wen], Wang, S.G.[Shu-Gen],
HCFPN: Hierarchical Contextual Feature-Preserved Network for Remote Sensing Scene Classification,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Wang, X.[Xuan], Zhu, Z.G.[Zhi-Gang],
Context understanding in computer vision: A survey,
CVIU(229), 2023, pp. 103646.
Elsevier DOI 2303
Context, Computer Vision, Deep Learning Models, Context Integration BibRef

Wang, X.[Xuan], Tang, H.[Hao], Zhu, Z.G.[Zhi-Gang],
GMC: A general framework of multi-stage context learning and utilization for visual detection tasks,
CVIU(241), 2024, pp. 103944.
Elsevier DOI 2403
Context, Context integration, Object detection, Pedestrian detection BibRef

Ramrakhya, R.[Ram], Kembhavi, A.[Aniruddha], Batra, D.[Dhruv], Kira, Z.[Zsolt], Zeng, K.H.[Kuo-Hao], Weihs, L.[Luca],
Seeing the Unseen: Visual Common Sense for Semantic Placement,
CVPR24(16273-16283)
IEEE DOI 2410
Visualization, Machine vision, Computational modeling, Semantics, Pipelines, Predictive models, Embodied AI, Common Sense Reasoning BibRef

Bhowmik, A.[Aritra], Wang, Y.[Yu], Baka, N.[Nora], Oswald, M.R.[Martin R.], Snoek, C.G.M.[Cees G. M.],
Detecting Objects with Context-Likelihood Graphs and Graph Refinement,
ICCV23(6501-6510)
IEEE DOI 2401
Objects in context. BibRef

Kim, B.[Byeonghwi], Kim, J.[Jinyeon], Kim, Y.[Yuyeong], Min, C.[Cheolhong], Choi, J.H.[Jong-Hyun],
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents,
ICCV23(10902-10912)
IEEE DOI 2401
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Zhang, Z.Y.[Zhong-Yan], Wang, L.[Lei], Zhou, L.P.[Lu-Ping], Koniusz, P.[Piotr],
Learning Spatial-Context-Aware Global Visual Feature Representation for Instance Image Retrieval,
ICCV23(11216-11225)
IEEE DOI Code:
WWW Link. 2401
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Chang, D.[Doosoo], Han, B.H.[Bo-Hyung],
Knowledge-based Visual Context-Aware Framework for Applications in Robotic Services,
RealWorld23(1-9)
IEEE DOI 2302
Visualization, Service robots, Surveillance, Scalability, Knowledge based systems, Context awareness, Knowledge representation BibRef

Jiang, H.X.[Han-Xiao], Mao, Y.S.[Yong-Sen], Savva, M.[Manolis], Chang, A.X.[Angel X.],
OPD: Single-View 3D Openable Part Detection,
ECCV22(XXIX:410-426).
Springer DOI 2211
What parts of an object can open and how they move when they do so. BibRef

Bomatter, P.[Philipp], Zhang, M.[Mengmi], Karev, D.[Dimitar], Madan, S.[Spandan], Tseng, C.[Claire], Kreiman, G.[Gabriel],
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes,
ICCV21(255-264)
IEEE DOI 2203
Visualization, Solid modeling, Computational modeling, Transformers, Cognition, Recognition and classification, Scene analysis and understanding BibRef

Ruan, D.S.[Dong-Sheng], Wang, D.[Daiyin], Zheng, Y.[Yuan], Zheng, N.G.[Neng-Gan], Zheng, M.[Min],
Gaussian Context Transformer,
CVPR21(15124-15133)
IEEE DOI 2111
Transforms, Detectors, Benchmark testing, Transformers, Solids BibRef

Seo, P.H.[Paul Hongsuck], Nagrani, A.[Arsha], Schmid, C.[Cordelia],
Look Before you Speak: Visually Contextualized Utterances,
CVPR21(16872-16882)
IEEE DOI 2111
Visualization, Annotations, Manuals, Benchmark testing, Transformers BibRef

Unal, M.E.[Mesut Erhan], Kovashka, A.[Adriana],
Context for Object Detection via Lightweight Global and Mid-level Representations,
ICPR21(8423-8430)
IEEE DOI 2105
Visualization, Annotations, Image edge detection, Semantics, Object detection, Switches, Linguistics BibRef

Palacio, S.[Sebastian], Engler, P.[Philipp], Hees, J.[Jörn], Dengel, A.[Andreas],
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks,
ICPR21(8937-8944)
IEEE DOI 2105
Neural networks, Focusing, Predictive models, Prediction algorithms, Minimization, Classification algorithms BibRef

Wu, T.Y.[Tz-Ying], Morgado, P.[Pedro], Wang, P.[Pei], Ho, C.H.[Chih-Hui], Vasconcelos, N.M.[Nuno M.],
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier,
ECCV20(VIII:171-189).
Springer DOI 2011
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Kim, M.C.[Min-Chul], Park, J.C.[Jong-Chan], Na, S.[Seil], Park, C.M.[Chang Min], Yoo, D.G.[Dong-Geun],
Learning Visual Context by Comparison,
ECCV20(V:576-592).
Springer DOI 2011
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Singh, K.K., Mahajan, D., Grauman, K., Lee, Y.J., Feiszli, M., Ghadiyaram, D.,
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias,
CVPR20(11067-11075)
IEEE DOI 2008
Context modeling, Visualization, Training, Decorrelation, Task analysis, Data models, Microwave imaging BibRef

Zhang, M., Tseng, C., Kreiman, G.,
Putting Visual Object Recognition in Context,
CVPR20(12982-12991)
IEEE DOI 2008
Visualization, Context modeling, Computational modeling, Task analysis, Object recognition, Feature extraction, Modulation BibRef

Liu, Y., Jia, X., Tan, M., Vemulapalli, R., Zhu, Y., Green, B., Wang, X.,
Search to Distill: Pearls Are Everywhere but Not the Eyes,
CVPR20(7536-7545)
IEEE DOI 2008
Task analysis, Knowledge engineering, Training, Neural networks, Computer architecture, Standards, Learning (artificial intelligence) BibRef

Choi, S., Kim, J.T., Choo, J.,
Cars Can't Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks,
CVPR20(9370-9380)
IEEE DOI 2008
Semantics, Image segmentation, Roads, Feature extraction, Computer architecture, Convolutional codes, Entropy BibRef

Muhammad, U.R.[Umar Riaz], Yang, Y.X.[Yong-Xin], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao], Song, Y.Z.[Yi-Zhe],
Goal-Driven Sequential Data Abstraction,
ICCV19(71-80)
IEEE DOI 2004
data handling, data structures, learning (artificial intelligence), automatic data abstraction, BibRef

Zhang, J.J.[Jun-Jie], Wu, Q.[Qi], Zhang, J.[Jian], Shen, C.H.[Chun-Hua], Lu, J.F.[Jian-Feng],
Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks,
CVPR19(2951-2959).
IEEE DOI 2002
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Shetty, R.[Rakshith], Schiele, B.[Bernt], Fritz, M.[Mario],
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation,
CVPR19(8210-8218).
IEEE DOI 2002
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Aggarwal, D.[Divyansh], Valiyev, E.[Elchin], Sener, F.[Fadime], Yao, A.[Angela],
Learning Style Compatibility for Furniture,
GCPR18(552-566).
Springer DOI 1905
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Nie, B., Sun, S.,
Joint Knowledge Base Embedding with Neighborhood Context,
ICPR18(379-384)
IEEE DOI 1812
Semantics, Knowledge based systems, Context modeling, Knowledge representation, Logic gates, Training, Task analysis BibRef

Abate, A.F.[Andrea F.], Nappi, M.[Michele], Barra, S.[Silvio], de Marsico, M.[Maria],
What are you doing while answering your smartphone?,
ICPR18(3120-3125)
IEEE DOI 1812
Accelerometers, Gyroscopes, Legged locomotion, Support vector machines, Ear, Intelligent sensors BibRef

Chen, X.L.[Xin-Lei], Li, L.J.[Li-Jia], Fei-Fei, L.[Li], Gupta, A.[Abhinav],
Iterative Visual Reasoning Beyond Convolutions,
CVPR18(7239-7248)
IEEE DOI 1812
Cognition, Semantics, Visualization, Image edge detection, Knowledge based systems, Computer architecture, Automobiles BibRef

Yu, F.[Fisher], Wang, D.Q.[De-Quan], Shelhamer, E.[Evan], Darrell, T.J.[Trevor J.],
Deep Layer Aggregation,
CVPR18(2403-2412)
IEEE DOI 1812
Across representation (low to high) levels. Semantics, Task analysis, Computer architecture, Visualization, Aggregates, Spatial resolution, Convolution BibRef

Wu, Z.R.[Zhi-Rong], Efros, A.A.[Alexei A.], Yu, S.X.[Stella X.],
Improving Generalization via Scalable Neighborhood Component Analysis,
ECCV18(VII: 712-728).
Springer DOI 1810
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Xie, Y.[Youye], Tang, Y.H.[Ying-Heng], Tang, G.G.[Gong-Guo], Hoff, W.A.[William A.],
Learning To Find Good Correspondences Of Multiple Objects,
ICPR21(2779-2786)
IEEE DOI 2105
Deep architecture, Prediction algorithms, Object recognition BibRef

Xie, Y.[Youye], Tang, G.G.[Gong-Guo], Hoff, W.A.[William A.],
Chess Piece Recognition Using Oriented Chamfer Matching with a Comparison to CNN,
WACV18(2001-2009)
IEEE DOI 1806
augmented reality, feature extraction, feedforward neural nets, image matching, image texture, BibRef

Malinowski, M.[Mateusz], Mokarian, A.[Ashkan], Fritz, M.[Mario],
Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task,
BMVC16(xx-yy).
HTML Version. 1805
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Yu, W.[Wei], Tao, Y.[Yi], Yao, H.X.[Hong-Xun],
The shortest matching path based on novel cycle consistency,
ICIP17(1975-1979)
IEEE DOI 1803
Feature extraction, Image matching, Optimization, Reliability, Search problems, Task analysis, CNN feature pyramid, Shortest path searching BibRef

Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., Pan, C.,
Context-aware cascade network for semantic labeling in VHR image,
ICIP17(575-579)
IEEE DOI 1803
Automobiles, Context, Convolution, Labeling, Semantics, Training, Vegetation, Context, Convolutional Neural Networks, VHR Image BibRef

Zhang, Y.[Yinda], Bai, M.R.[Ming-Ru], Kohli, P.[Pushmeet], Izadi, S.[Shahram], Xiao, J.X.[Jian-Xiong],
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding,
ICCV17(1201-1210)
IEEE DOI 1802
CAD, convolution, neural nets, object detection, object recognition, rendering (computer graphics), solid modelling, Training data BibRef

Hung, W.C., Tsai, Y.H., Shen, X., Lin, Z., Sunkavalli, K., Lu, X., Yang, M.H.,
Scene Parsing with Global Context Embedding,
ICCV17(2650-2658)
IEEE DOI 1802
Context modeling, Encoding, Image coding, Image segmentation, Measurement, Semantics BibRef

Apicella, A.[Andrea], Corazza, A.[Anna], Isgrò, F.[Francesco], Vettigli, G.[Giuseppe],
Exploiting Context Information for Image Description,
CIAP17(I:320-331).
Springer DOI 1711
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Misra, I.[Ishan], Gupta, A.[Abhinav], Hebert, M.[Martial],
From Red Wine to Red Tomato: Composition with Context,
CVPR17(1160-1169)
IEEE DOI 1711
Automobiles, Context modeling, Training, Training data, Transforms, Visualization BibRef

Abdulnabi, A.H., Shuai, B., Winkler, S., Wang, G.,
Episodic CAMN: Contextual Attention-Based Memory Networks with Iterative Feedback for Scene Labeling,
CVPR17(6278-6287)
IEEE DOI 1711
Adaptation models, Context modeling, Convolution, Feature extraction, Labeling, Semantics BibRef

Marino, K., Salakhutdinov, R., Gupta, A.,
The More You Know: Using Knowledge Graphs for Image Classification,
CVPR17(20-28)
IEEE DOI 1711
Automobiles, Bicycles, Mice, Propagation, losses BibRef

Vittayakorn, S.[Sirion], Berg, A.C.[Alexander C.], Berg, T.L.[Tamara L.],
When Was That Made?,
WACV17(715-724)
IEEE DOI 1609
Deep learning. Automobiles, Clothing, Estimation, Feature extraction, Flickr, Machine learning, Visualization BibRef

Shahriari, M., Bergevin, R.,
A Two-Stage Outdoor-Indoor Scene Classification Framework: Experimental Study for the Outdoor Stage,
DICTA16(1-8)
IEEE DOI 1701
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And:
Can Contextual Information Improve Scene Classification Performance?,
DICTA16(1-7)
IEEE DOI 1701
Buildings. Context BibRef

Volokitin, A.[Anna], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Deep Features or Not: Temperature and Time Prediction in Outdoor Scenes,
Robust16(1136-1144)
IEEE DOI 1612

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Singh, S.[Saurabh], Hoiem, D.[Derek], Forsyth, D.A.[David A.],
Learning to Localize Little Landmarks,
CVPR16(260-269)
IEEE DOI 1612
Light switch, door handle. BibRef

Liu, B.[Buyu], He, X.M.[Xu-Ming],
Learning Dynamic Hierarchical Models for Anytime Scene Labeling,
ECCV16(VI: 650-666).
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Time tradeoffs vs. accuracy. BibRef

Bappy, J.H.[Jawadul H.], Paul, S.[Sujoy], Roy-Chowdhury, A.K.[Amit K.],
Online Adaptation for Joint Scene and Object Classification,
ECCV16(VIII: 227-243).
Springer DOI 1611
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Chen, X.S., He, H., Davis, L.S.,
Object detection in 20 questions,
WACV16(1-9)
IEEE DOI 1606
Context BibRef

Johnson, J.[Justin], Ballan, L.[Lamberto], Fei-Fei, L.[Li],
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata,
ICCV15(4624-4632)
IEEE DOI 1602
Recognize using social network metadata. BibRef

Glasner, D.[Daniel], Fua, P.[Pascal], Zickler, T.E.[Todd E.], Zelnik-Manor, L.[Lihi],
Hot or Not: Exploring Correlations between Appearance and Temperature,
ICCV15(3997-4005)
IEEE DOI 1602
Cameras. E.g. foliage, reflective oriented surfaces. Heat induced vibrations.
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Liang, X.D.[Xiao-Dan], Liu, S.[Si], Wei, Y.C.[Yun-Chao], Liu, L.Q.[Luo-Qi], Lin, L.[Liang], Yan, S.C.[Shui-Cheng],
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection,
ICCV15(999-1007)
IEEE DOI 1602
Computational modeling. Learn a few examples with CNN, concept from these. BibRef

Cadík, M.[Martin], Vašícek, J.[Jan], Hradiš, M.[Michal], Radenovic, F.[Filip], Chum, O.[Ondrej],
Camera Elevation Estimation from a Single Mountain Landscape Photograph,
BMVC15(xx-yy).
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Elevation about sea level. Based on features. BibRef

Wang, X.G.[Xing-Gang], Yang, X.[Xin], Liu, W.Y.[Wen-Yu], Duan, C.[Chen], Latecki, L.J.[Longin Jan],
Location-Aware Image Classification,
MMMod16(I: 829-841).
Springer DOI 1601
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Zhang, C.Y.[Cheng-Yue], Han, Y.H.[Ya-Hong],
Describing Images with Ontology-Aware Dictionary Learning,
MMMod16(I: 349-358).
Springer DOI 1601
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Caesar, H.[Holger], Uijlings, J.[Jasper], Ferrari, V.[Vittorio],
COCO-Stuff: Thing and Stuff Classes in Context,
CVPR18(1209-1218)
IEEE DOI 1812
Protocols, Bridges, Vegetation mapping, Automobiles, Semantics, Image segmentation, Shape BibRef

Gonzalez-Garcia, A., Modolo, D., Ferrari, V.,
Objects as Context for Detecting Their Semantic Parts,
CVPR18(6907-6916)
IEEE DOI 1812
Proposals, Semantics, Wheels, Automobiles, Context modeling, Task analysis, Object detection BibRef

Uijlings, J., Konyushkova, K., Lampert, C.H., Ferrari, V.,
Learning Intelligent Dialogs for Bounding Box Annotation,
CVPR18(9175-9184)
IEEE DOI 1812
Detectors, Manuals, Cats, Proposals, Image segmentation BibRef

Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio],
Object localization in ImageNet by looking out of the window,
BMVC15(xx-yy).
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Not just features in the sliding window. BibRef

Gonzalez-Garcia, A.[Abel], Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio],
An active search strategy for efficient object class detection,
CVPR15(3022-3031)
IEEE DOI 1510
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Codevilla, F.[Felipe], Botelho, S.S.C.[Silvia S. C.], Duarte, N.[Nelson], Purkis, S.[Samuel], Shihavuddin, A.S.M., Garcia, R.[Rafael], Gracias, N.[Nuno],
Geostatistics for Context-Aware Image Classification,
CVS15(228-239).
Springer DOI 1507
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Terayama, K.[Kei], Hioki, H.[Hirohisa],
A practical classifier for photographs and non-photographic images based on local visual features,
MVA15(307-311)
IEEE DOI 1507
Is it a photograph or some other source. BibRef

Lang, H.T.[Hai-Tao], Xi, Y.Y.[Yu-Yang], Hu, J.Y.[Jian-Ying], Du, L.[Liang], Ling, H.B.[Hai-Bin],
Scene Classification by Feature Co-occurrence Matrix,
AutoSystems14(501-510).
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e.g. mountain vs. forest. BibRef

Li, S., Chen, C.[Chen], Ren, Y.Z.[Yu-Zhuo], Kuo, C.C.J.[C.C. Jay],
Improving Object Classification Performance via Confusing Categories Study,
WACV18(1774-1783)
IEEE DOI 1806
convolution, feature extraction, feedforward neural nets, image classification, learning (artificial intelligence), Visualization BibRef

Chen, C.[Chen], Ren, Y.Z.[Yu-Zhuo], Kuo, C.C.J.[C.C. Jay],
Large-Scale Indoor/Outdoor Image Classification via Expert Decision Fusion (EDF),
AutoSystems14(426-442).
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Tucker, J.D.[Jonathan D.], Stanfill, S.R.[S. Robert],
Context Exploitation in Intelligence, Surveillance, and Reconnaissance for Detection Algorithms,
AAVE15(13-20)
IEEE DOI 1503
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Dibra, E.[Endri], Maye, J.[Jerome], Diamanti, O.[Olga], Siegwart, R.[Roland], Beardsley, P.[Paul],
Extending the Performance of Human Classifiers Using a Viewpoint Specific Approach,
WACV15(765-772)
IEEE DOI 1503
Cameras BibRef

Mathews, A.[Alexander], Xie, L.X.[Le-Xing], He, X.M.[Xu-Ming],
Choosing Basic-Level Concept Names Using Visual and Language Context,
WACV15(595-602)
IEEE DOI 1503
Context BibRef

Klaghstan, M., Haensch, R., Coquil, D., Hellwich, O.,
Impact of hierarchical structures in image categorization systems,
IPTA12(367-370)
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feature extraction. Divide categorization problem into smaller problems. BibRef

Jung, H.J.[Ho-Jung], Kurazume, R.[Ryo], Iwashita, Y.[Yumi], Mozos, O.M.[Oscar Martinez],
Two-dimensional local ternary patterns using synchronized images for outdoor place categorization,
ICIP14(5726-5730)
IEEE DOI 1502
Histograms. Texture and depth combineed. Categorize outdoor scenes by type. BibRef

Albaradei, S.[Somayah], Wang, Y.[Yang],
Object Classification Using a Semantic Hierarchy,
ISVC14(I: 228-237).
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Chen, Z.H.[Zeng-Hai], Chi, Z.[Zheru], Fu, H.[Hong],
A Hybrid Holistic/Semantic Approach for Scene Classification,
ICPR14(2299-2304)
IEEE DOI 1412
Global features vs. features of parts. BibRef

Lawson, W.[Wallace], Hiatt, L.[Laura], Trafton, J.G.[J. Gregory],
Leveraging Cognitive Context for Object Recognition,
Cognition14(387-392)
IEEE DOI 1409
ACT-R; Contextual Information; LVis; Neural Networks; Object Recognition BibRef

Xie, L.X.[Ling-Xi], Wang, J.D.[Jing-Dong], Guo, B.N.[Bai-Ning], Zhang, B.[Bo], Tian, Q.[Qi],
Orientational Pyramid Matching for Recognizing Indoor Scenes,
CVPR14(3734-3741)
IEEE DOI 1409
Orientational Pyramid Matching BibRef

Zhu, X.X.[Xiang-Xin], Anguelov, D.[Dragomir], Ramanan, D.[Deva],
Capturing Long-Tail Distributions of Object Subcategories,
CVPR14(915-922)
IEEE DOI 1409
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Wang, X.L.[Xiao-Long], Gupta, A.[Abhinav],
Generative Image Modeling Using Style and Structure Adversarial Networks,
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Style (texture mapping), structure (3D model). BibRef

Doersch, C.[Carl], Gupta, A.[Abhinav], Efros, A.A.[Alexei A.],
Context as Supervisory Signal: Discovering Objects with Predictable Context,
ECCV14(III: 362-377).
Springer DOI 1408
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Maeda, D., Morimoto, M.,
An Object Recognition Method Using RGB-D Sensor,
ACPR13(857-861)
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feature extraction. Within categories. BibRef

Wan, S.H.[Shao-Hua], Aggarwal, J.K.,
Scene recognition by jointly modeling latent topics,
WACV14(706-713)
IEEE DOI 1406
recognize scene categories. BibRef

Lin, D.[Dahua], Xiao, J.X.[Jian-Xiong],
Characterizing Layouts of Outdoor Scenes Using Spatial Topic Processes,
ICCV13(841-848)
IEEE DOI 1403
BibRef

Pan, J.[Jiyan], Kanade, T.[Takeo],
Coherent Object Detection with 3D Geometric Context from a Single Image,
ICCV13(2576-2583)
IEEE DOI 1403
3D geometric context; object detection BibRef

Shao, M.[Ming], Li, L.Y.[Liang-Yue], Fu, Y.[Yun],
What Do You Do? Occupation Recognition in a Photo via Social Context,
ICCV13(3631-3638)
IEEE DOI 1403
Occupation Recognition; Social Context; Visual Attributes BibRef

Kristan, M.[Matej], Boben, M.[Marko], Tabernik, D.[Domen], Leonardis, A.[Ales],
Adding Discriminative Power to Hierarchical Compositional Models for Object Class Detection,
SCIA13(444-455).
Springer DOI 1311
BibRef

Govender, N.[Natasha], Claassens, J.[Jonathan], Nicolls, F.[Fred], Warrell, J.[Jonathan],
Active object recognition using vocabulary trees,
WORV13(20-26)
IEEE DOI 1307
BibRef

Douillard, B., Quadros, A., Morton, P., Underwood, J.P., de Deuge, M.,
A 3D classifier trained without field samples,
ICARCV12(805-810).
IEEE DOI 1304
Detecte ground surface first. BibRef

Park, S.[Sangdon], Kim, W.S.[Won-Sik], Lee, K.M.[Kyoung Mu],
Abnormal Object Detection by Canonical Scene-Based Contextual Model,
ECCV12(III: 651-664).
Springer DOI 1210
BibRef

Lang, H.T.[Hai-Tao], Ling, H.B.[Hai-Bin],
Classifying covert photographs,
CVPR12(1178-1185).
IEEE DOI 1208
Features that are different, composition, blur, etc. BibRef

Nwogu, I.[Ifeoma], Zhou, Y.B.[Ying-Bo], Brown, C.[Christopher],
An ontology for generating descriptions about natural outdoor scenes,
SIG11(656-663).
IEEE DOI 1201
BibRef

Li, C.C.[Cong-Cong], Parikh, D.[Devi], Chen, T.H.[Tsu-Han],
Automatic discovery of groups of objects for scene understanding,
CVPR12(2735-2742).
IEEE DOI 1208
BibRef
Earlier:
Extracting adaptive contextual cues from unlabeled regions,
ICCV11(511-518).
IEEE DOI 1201
BibRef

Parikh, D.[Devi],
Recognizing jumbled images: The role of local and global information in image classification,
ICCV11(519-526).
IEEE DOI 1201
BibRef

Bannour, H.[Hichem], Hudelot, C.[Céline],
Building Semantic Hierarchies Faithful to Image Semantics,
MMMod12(4-15).
Springer DOI 1201
BibRef

Bergström, N.[Niklas], Ek, C.H.[Carl Henrik], Björkman, M.[Mårten], Kragic, D.[Danica],
Scene Understanding through Autonomous Interactive Perception,
CVS11(153-162).
Springer DOI 1109
BibRef

Wang, W.N.[Wei-Ning], Yi, J.J.[Jing-Jian], Li, H.P.[Hao-Pan], Lu, Y.[Yin],
Local Semantic Classification of Natural Image Based on Spatial Context,
ICIG11(482-487).
IEEE DOI 1109
BibRef

Perina, A.[Alessandro], Jojic, N.[Nebojsa],
Image analysis by counting on a grid,
CVPR11(1985-1992).
IEEE DOI 1106
Not just a bag of features, but location matters. BibRef

del Pero, L.[Luca], Guan, J.Y.[Jin-Yan], Brau, E.[Ernesto], Schlecht, J.[Joseph], Barnard, K.[Kobus],
Sampling bedrooms,
CVPR11(2009-2016).
IEEE DOI 1106
Understand indoor scenes. Lots of planar orthogonal surfaces, and boxes. Model using only boxes. BibRef

Huang, Q.X.[Qi-Xing], Han, M.[Mei], Wu, B.[Bo], Ioffe, S.[Sergey],
A hierarchical conditional random field model for labeling and segmenting images of street scenes,
CVPR11(1953-1960).
IEEE DOI 1106
BibRef

Aboutalib, S.[Sarah],
Multiple-Cue Object Recognition for interactionable Objects,
CMU-CS-10-153, December 2010. BibRef 1012 Ph.D.Thesis, CMU, December 2010
HTML Version. 1102
multiple intrinsic cues or human annotation. BibRef

Leibo, J.Z.[Joel Z.], Mutch, J.[Jim], Rosasco, L.[Lorenzo], Ullman, S.[Shimon], Poggio, T.[Tomaso],
Learning Generic Invariances in Object Recognition: Translation and Scale,
CSAIL(TR-2010-061). 2010-12-30
WWW Link. 1101
BibRef

Leibo, J.Z.[Joel Z.], Mutch, J.[Jim], Ullman, S.[Shimon], Poggio, T.[Tomaso],
From primal templates to invariant recognition,
CSAIL(TR-2010-057). 2010-12-04
WWW Link. 1101
BibRef

Baba, T.[Takayuki], Chen, T.H.[Tsu-Han],
Object-driven image group annotation,
ICIP10(2641-2644).
IEEE DOI 1009
Separate by time (so similar objects) BibRef

Rohrbach, M.[Marcus], Stark, M.[Michael], Schiele, B.[Bernt],
Evaluating knowledge transfer and zero-shot learning in a large-scale setting,
CVPR11(1641-1648).
IEEE DOI 1106
BibRef

Rohrbach, M.[Marcus], Stark, M.[Michael], Szarvas, G.[Gyorgy], Gurevych, I.[Iryna], Schiele, B.[Bernt],
What helps where - and why? Semantic relatedness for knowledge transfer,
CVPR10(910-917).
IEEE DOI Video of talk:
WWW Link. 1006
Scale recognition to large numbers of classes. BibRef

Semenovich, D.[Dimitri], Sowmya, A.[Arcot],
Tensor Power Method for Efficient MAP Inference in Higher-order MRFs,
ICPR10(734-737).
IEEE DOI 1008
BibRef

Semenovich, D.[Dimitri], Sowmya, A.[Arcot],
A spectral method for context based disambiguation of image annotations,
ICIP09(789-792).
IEEE DOI 0911
BibRef

Parizi, S.N.[Sobhan Naderi], Laptev, I.[Ivan], Targhi, A.T.[Alireza Tavakoli],
Modeling Image Context Using Object Centered Grid,
DICTA09(476-483).
IEEE DOI 0912
BibRef

Viswanathan, P.[Pooja], Southey, T.[Tristram], Little, J.J.[James J.], Mackworth, A.K.[Alan K.],
Place Classification Using Visual Object Categorization and Global Information,
CRV11(1-7).
IEEE DOI 1105
BibRef
Earlier:
Automated Place Classification Using Object Detection,
CRV10(324-330).
IEEE DOI 1005
BibRef

Viswanathan, P.[Pooja], Meger, D.[David], Southey, T.[Tristram], Little, J.J.[James J.], Mackworth, A.K.[Alan K.],
Automated Spatial-Semantic Modeling with Applications to Place Labeling and Informed Search,
CRV09(284-291).
IEEE DOI 0905
BibRef

Li, L.J.[Li-Jia], Socher, R.[Richard], Fei-Fei, L.[Li],
Towards total scene understanding: Classification, annotation and segmentation in an automatic framework,
CVPR09(2036-2043).
IEEE DOI 0906
Classify general category, segment and annotate individual objects (regions and patches). Apply to 3 sports scenes. BibRef

Socher, R.[Richard], Fei-Fei, L.[Li],
Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora,
CVPR10(966-973).
IEEE DOI 1006
BibRef

Hays, J.H.[James H.],
Large Scale Scene Matching for Graphics and Vision,
CMU-CS-09-152, July 2009. BibRef 0907 Ph.D.Thesis, CMU, 2009.
HTML Version. 1102
BibRef

Divvala, S.K.[Santosh K.], Hoiem, D.[Derek], Hays, J.H.[James H.], Efros, A.A.[Alexei A.], Hebert, M.[Martial],
An empirical study of context in object detection,
CVPR09(1271-1278).
IEEE DOI 0906
BibRef

Lazebnik, S.[Svetlana], Raginsky, M.[Maxim],
An empirical Bayes approach to contextual region classification,
CVPR09(2380-2387).
IEEE DOI 0906
BibRef

Ross, S.[Stephane], Munoz, D.[Daniel], Hebert, M.[Martial], Bagnell, J.A.[J. Andrew],
Learning message-passing inference machines for structured prediction,
CVPR11(2737-2744).
IEEE DOI 1106
For approximate inference in hierarchical/sequential labeling system. message-passing: Belief Propogation. BibRef

Munoz, D.[Daniel], Bagnell, J.A.[J. Andrew], Hebert, M.[Martial],
Stacked Hierarchical Labeling,
ECCV10(VI: 57-70).
Springer DOI 1009
BibRef

Munoz, D.[Daniel], Bagnell, J.A.[J. Andrew], Vandapel, N.[Nicolas], Hebert, M.[Martial],
Contextual classification with functional Max-Margin Markov Networks,
CVPR09(975-982).
IEEE DOI 0906
BibRef

Chong, W.[Wang], Blei, D.M.[David M.], Fei-Fei, L.[Li],
Simultaneous image classification and annotation,
CVPR09(1903-1910).
IEEE DOI 0906
Class label is a global descriptor, annotation is local descriptor. BibRef

Rabinovich, A.[Andrew], Belongie, S.J.[Serge J.],
Scenes vs. objects: A comparative study of two approaches to context based recognition,
VCL-ViSU09(92-99).
IEEE DOI 0906
BibRef

Dong, L.[Le], Izquierdo, E.[Ebroul],
Global-to-local oriented perception on blurry visual information,
ICIP08(2168-2171).
IEEE DOI 0810
BibRef

McDaniel, T.L.[Troy L.], Kahol, K.[Kanav], Panchanathan, S.[Sethuraman],
A Bayesian Approach to Visual Size Classification of Everyday Objects,
ICPR06(II: 255-259).
IEEE DOI 0609
BibRef

Kumar, S.[Sanjiv], Hebert, M.[Martial],
A Hierarchical Field Framework for Unified Context-Based Classification,
ICCV05(II: 1284-1291).
IEEE DOI 0510
BibRef

Kumar, S.[Sanjiv],
Models for Learning Spatial Interactions in Natural Images for Context-Based Classification,
CMU-CS-05-28, Robotics Institute, August, 2005.
WWW Link. BibRef 0508

Morgenstern, C.[Christian], Heisele, B.[Bernd],
Component based recognition of objects in an office environment,
MIT AIM-2003-024, November 28, 2003.
WWW Link. 0501
BibRef

Schlecht, J.[Joseph], Barnard, K.[Kobus], Pryor, B.[Barry],
Statistical Inference of Biological Structure and Point Spread Functions in 3D Microscopy,
3DPVT06(373-380).
IEEE DOI 0606
BibRef

Louie, J.[Jennifer],
A Biological Model of Object Recognition with Feature Learning,
MIT AI-TR-2003-009, May 28, 2003. BibRef 0305 Ph.D.Thesis. 2003.
WWW Link. This thesis presents a new model that integrates learning of object-specific features with the HMAX of Riesenhuber and Poggio 0306
BibRef

Serre, T.[Thomas],
Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines,
CSAIL-2006-028, April 2006.
WWW Link. BibRef 0604

Serre, T.[Thomas], Wolf, L.B.[Lior B.], Poggio, T.[Tomaso],
A new biologically motivated framework for robust object recognition,
MIT AIM-2004-026, November 14, 2004.
WWW Link. 0501
BibRef

Serre, T.[Thomas], Riesenhuber, M.[Maximilian], Louie, J.[Jennifer], Poggio, T.[Tomaso],
On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision,
BMCV02(387 ff.).
Springer DOI 0303

See also Robust Object Recognition with Cortex-Like Mechanisms. BibRef

Pinhanez, C.[Claudio], Bobick, A.F.[Aaron F.],
Using Approximate Models as Source of Contextual Information for Vision Processing,
Context95(xx) BibRef 9500

Hild, M., and Shirai, Y.,
Interpretation of Natural Scenes Using Multi-Parameter Default Models and Qualitative Constraints,
ICCV93(497-501).
IEEE DOI Looks like a basic recognition scheme like early Ohta work and UMass work. BibRef 9300

Kadono, K., Asada, M., Shirai, Y.,
Context-Constrained Matching of Hierarchical CAD-Based Models for Outdoor Scene Interpretation,
CADBV91(186-195). BibRef 9100

Hild, M., Shirai, Y., Asada, M.,
Initial Segmentation For Knowledge Indexing,
ICPR92(I:587-590).
IEEE DOI BibRef 9200

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Context, Fine-Grained Classification .