HOGgles: Visualizing Object Detection Features
Open accessAbstract
We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
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There is a significant amount of work in understanding the representation of deep neural networks by means of artifact images.
“There is a significant amount of work in understanding the representation of DNN by means of artifact images, [41,100,105,111].”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [100]
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Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321 · see citing works
Self-citationImage Super-Resolution Via Sparse Representation
Yang, J., Wright, J. N., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,241 · see citing works
BRIEF: Binary Robust Independent Elementary Features
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_56 · Cited by 3,671 · see citing works
Efficient sparse coding algorithms
Lee, H., Battle, A. J., Raina, R., & Ng, A. Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0105 · Cited by 2,449 · see citing works
Online dictionary learning for sparse coding
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553463 · Cited by 2,147 · see citing works
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247715 · Cited by 1,791 · see citing works
Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies
Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011) · Current Biology · doi:10.1016/j.cub.2011.08.031 · Cited by 933 · see citing works
Cascade object detection with deformable part models
Felzenszwalb, P. F., Girshick, R., & McAllester, D. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539906 · Cited by 929 · see citing works
Ensemble of exemplar-SVMs for object detection and beyond
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865 · see citing works
Self-citationWang, S., Lei Zhang, A., Liang, Y., & Pan, Q. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247930 · Cited by 542 · see citing works
Diagnosing Error in Object Detectors
Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_25 · Cited by 520 · see citing works
Discriminative Decorrelation for Clustering and Classification
Hariharan, B., Malik, J., & Ramanan, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_33 · Cited by 271 · see citing works
SENSC: a Stable and Efficient Algorithm for Nonnegative Sparse Coding
Li, L., & Zhang, Y. (2009) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2009.01257 · Cited by 223 · see citing works
Do We Need More Training Data or Better Models for Object Detection?
Zhu, X., Vondrick, C., Ramanan, D., & Fowlkes, C. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.80 · Cited by 178 · cited 1× · see citing works
“[24] try to determine whether we have reached Bayes risk for HOG.”
BackgroundSelf-citationReconstructing an image from its local descriptors
Weinzaepfel, P., Jegou, H., & Perez, P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995616 · Cited by 159 · see citing works
How Important Are “Deformable Parts” in the Deformable Parts Model?
Divvala, S., Efros, A. A., & Hebert, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33885-4_4 · Cited by 105 · see citing works
The role of features, algorithms and data in visual recognition
Parikh, D., & Zitnick, C. L. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539920 · Cited by 49 · see citing works
Beyond Bits: Reconstructing Images from Local Binary Descriptors
d'Angelo, E., Alahi, A., & Vandergheynst, P. (2012) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 40 · see citing works
Liu, L., & Wang, L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248103 · Cited by 19 · see citing works
Exploring the representation capabilities of the HOG descriptor
Tatu, A., Lauze, F., Nielsen, M., & Kimia, B. B. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130416 · Cited by 14 · see citing works
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works
Visualizing and Understanding Neural Models in NLP
Li, J., Chen, X., Hovy, E. H., & Jurafsky, D. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1082 · Cited by 673 · see citing works
Inverting Visual Representations with Convolutional Networks
Dosovitskiy, A., & Brox, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.522 · Cited by 631 · see citing works
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582 · see citing works
A Convolutional Approach for Misinformation Identification
Yu, F., Liu, Q., Wu, S., Wang, L., & Tan, T. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/545 · Cited by 530 · see citing works
Visualizing Deep Convolutional Neural Networks Using Natural Pre-images
Mahendran, A., & Vedaldi, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0911-8 · Cited by 487 · see citing works
Using deep learning to model the hierarchical structure and function of a cell
Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods · doi:10.1038/nmeth.4627 · Cited by 435 · see citing works
Sliding Shapes for 3D Object Detection in Depth Images
Song, S., & Xiao, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_41 · Cited by 361 · see citing works
Ruxton, G. D., Allen, W. L., Sherratt, T. N., & Speed, M. P. (2018) · doi:10.1093/oso/9780199688678.001.0001 · Cited by 355 · see citing works
Grad-CAM: Why did you say that?Preprint
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016) · arXiv.org · doi:10.48550/arxiv.1611.07450 · Cited by 646 · see citing works
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines
Song, S., & Xiao, J. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.36 · Cited by 275 · see citing works
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298701 · Cited by 366 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
“elated to producing a certain prediction by realizing a sort of articial image prototype. There is a signicant amount of work in understanding the representation of DNN by means of artifact images, [41,100,105,111]. We conclude this section presenting how [25] deals with artifacts in DNNs. Finding a single representat…”
BackgroundMulti-scale Recognition with DAG-CNNs
Yang, S., & Ramanan, D. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.144 · Cited by 192 · see citing works
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Castrejon, L., Aytar, Y., Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.321 · Cited by 188 · see citing works
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Zhang, D., Han, J., Li, C., & Wang, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298918 · Cited by 119 · see citing works
Aytar, Y., Castrejón, L., Vondrick, C., Pirsiavash, H., & Torralba, A. B. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2753232 · Cited by 123 · see citing works
Self-citationRevealing Scenes by Inverting Structure From Motion Reconstructions
Pittaluga, F., Koppal, S. J., Kang, S. B., & Sinha, S. N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00023 · Cited by 115 · see citing works
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Zeng, Y., Xu, X., Shen, D., Fang, Y., & Xiao, Z. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2614916 · Cited by 111 · see citing works
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Xu, Z., Tao, D., Zhang, Y., Wu, J., & Tsoi, A. C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_39 · Cited by 99 · see citing works
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Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298699 · Cited by 86 · see citing works
Yu, F., Liu, Q., Wu, S., Wang, L., & Tan, T. (2019) · Computers & Security · doi:10.1016/j.cose.2019.02.003 · Cited by 82 · see citing works
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Yin, B., Tran, L., Li, H., Shen, X., & Liu, X. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00944 · Cited by 90 · see citing works
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Dosovitskiy, A., & Brox, T. (2015) · arXiv (Cornell University) · Cited by 75 · see citing works
HERS: Homomorphically Encrypted Representation Search
Engelsma, J. J., Jain, A. K., & Boddeti, V. N. (2022) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3139866 · Cited by 73 · see citing works
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Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2016) · Cited by 72 · see citing works
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Mahendran, A., & Vedaldi, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.0035 · Cited by 105 · see citing works
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Ouyang, S., Hospedales, T. M., Song, Y.-Z., & Li, X. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.601 · Cited by 69 · see citing works
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Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_6 · Cited by 72 · see citing works
Image Reconstruction from Bag-of-Visual-Words
Kato, H., & Harada, T. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.127 · Cited by 81 · see citing works
Object tracking on event cameras with offline–online learning
Jiang, R., Mou, X., Shi, S., Zhou, Y., Wang, Q., Dong, M., & Chen, S. (2020) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2019.0107 · Cited by 65 · see citing works
Visual explanations from spiking neural networks using inter-spike intervals
Kim, Y., & Panda, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-98448-0 · Cited by 60 · see citing works
Auto-adjusting camera exposure for outdoor robotics using gradient information
Shim, I., Lee, J., & Kweon, I. S. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942682 · Cited by 58 · see citing works
Expected similarity estimation for large-scale batch and streaming anomaly detection
Schneider, M., Ertel, W., & Ramos, F. T. (2016) · Machine Learning · doi:10.1007/s10994-016-5567-7 · Cited by 58 · see citing works
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Shim, I., Choi, J., Shin, S., Oh, T.-H., Lee, U., Ahn, B., Choi, D., Shim, D. H., & Kweon, I. S. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2389237 · Cited by 54 · see citing works
Head detection and orientation estimation for pedestrian safety
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Lee, K. L., & Mokji, M. M. (2014) · International Conference on Electronic Design · doi:10.1109/iced.2014.7015795 · Cited by 51 · see citing works
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Grün, F., Rupprecht, C., Navab, N., & Tombari, F. (2016) · arXiv · doi:10.48550/arxiv.1606.07757 · Cited by 49 · see citing works
Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms
Shim, I., Oh, T.-H., Lee, J., Choi, J., Choi, D., & Kweon, I. S. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2846292 · Cited by 48 · see citing works
Salay, R., & Czarnecki, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.01614 · Cited by 47 · see citing works
Qing, Y. A. O., Guo-te, C. H. E. N., Wang, Z., Zhang, C., Yang, B., & Tang, J. (2017) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(16)61497-1 · Cited by 45 · see citing works
Ground Truth Data, Content, Metrics, and Analysis
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DCT Inspired Feature Transform for Image Retrieval and Reconstruction
Wang, Y., Shi, M., You, S., & Xu, C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2590323 · Cited by 40 · see citing works
MC-HOG Correlation Tracking with Saliency Proposal
Zhu, G., Wang, J., Wu, Y., Zhang, X., & Lu, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10450 · Cited by 41 · see citing works
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Geppert, M., Larsson, V., Speciale, P., Schönberger, J. L., & Pollefeys, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_20 · Cited by 40 · see citing works
Context as Supervisory Signal: Discovering Objects with Predictable Context
Doersch, C., Gupta, A., & Efros, A. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_24 · Cited by 39 · see citing works
Privacy Preserving Image Queries for Camera Localization
Speciale, P., Schönberger, J. L., Sinha, S. N., & Pollefeys, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00157 · Cited by 39 · see citing works
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Vondrick, C., Khosla, A., Pirsiavash, H., Malisiewicz, T., & Torralba, A. B. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0884-7 · Cited by 38 · see citing works
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Negative results in computer vision: A perspective
Borji, A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.10.001 · Cited by 38 · see citing works
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Riggan, B. S., Short, N. J., & Hu, S. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00010 · Cited by 36 · see citing works
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Bristow, H., & Lucey, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2407 · Cited by 36 · see citing works
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_2 · Cited by 36 · see citing works
Semantic Perceptual Image Compression With a Laplacian Pyramid of Convolutional Networks
Wang, J., Duan, Y., Tao, X., Xu, M., & Lü, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3065244 · Cited by 35 · see citing works
Interest Point Detector and Feature Descriptor Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_6 · Cited by 36 · see citing works
Enriching object detection with 2D-3D registration and continuous viewpoint estimation
Choy, C., Stark, M. M., Corbett‐Davies, S., & Savarese, S. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298866 · Cited by 34 · see citing works
Review on One-Stage Object Detection Based on Deep Learning
Zhang, H., & Cloutier, R. S. (2022) · ICST Transactions on e-Education and e-Learning · doi:10.4108/eai.9-6-2022.174181 · Cited by 35 · see citing works
Estimation of visible spectrum faces from polarimetric thermal faces
Riggan, B. S., Short, N. J., Hu, S., & Kwon, H. (2016) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2016.7791170 · Cited by 32 · see citing works
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
Ng, T., Kim, H. J., Lee, V. T., DeTone, D., Yang, T.-Y., Shen, T., Ilg, E., Balntas, V., Mikolajczyk, K., & Sweeney, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01246 · Cited by 32 · see citing works
Privacy Leakage of SIFT Features via Deep Generative Model Based Image Reconstruction
Wu, H., & Zhou, J. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3070427 · Cited by 30 · see citing works
Research on Image Generation and Style Transfer Algorithm Based on Deep Learning
Wang, R. (2019) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2019.98053 · Cited by 29 · see citing works
Inferring Visual Persuasion via Body Language, Setting, and Deep Features
Huang, X., & Kovashka, A. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.102 · Cited by 30 · see citing works
Human Action Recognition Based on DMMs, HOGs and Contourlet Transform
Bulbul, M. F., Jiang, Y., & Ma, J. (2015) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2015.82 · Cited by 28 · see citing works
From Bits to Images: Inversion of Local Binary Descriptors
d'Angelo, E., Jacques, L., Alahi, A., & Vandergheynst, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.228 · Cited by 27 · see citing works
Attributes for Image Retrieval
Kovashka, A., & Grauman, K. L. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-50077-5_5 · Cited by 28 · see citing works
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
Chelani, K., Kahl, F., & Sattler, T. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01541 · Cited by 28 · see citing works
Cooperative Learning with Visual AttributesPreprint
Batra, T., & Parikh, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05512 · Cited by 28 · see citing works
IcoRating: A Deep-Learning System for Scam ICO IdentificationPreprint
Bian, S., Deng, Z., Li, F., Monroe, W., Shi, P. J., Sun, Z., Wu, W., Wang, S., Wang, W. Y., Yuan, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.03670 · Cited by 27 · see citing works
TRANSLATING AERIAL IMAGES INTO STREET-MAP-LIKE REPRESENTATIONS FOR VISUAL SELF-LOCALIZATION OF UAVS
Schleiss, M. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w13-575-2019 · Cited by 26 · see citing works
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Hazarika, S., Li, H., Wang, K.-C., Shen, H., & Chou, C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934591 · Cited by 26 · see citing works
Vision-Based People Detection System for Heavy Machine Applications
Frémont, V., Bui, M. T., Djamal Boukerroui, A., & Letort, P. (2016) · Sensors · doi:10.3390/s16010128 · Cited by 26 · see citing works
A new descriptor of gradients Self-Similarity for smile detection in unconstrained scenarios
Gao, Y., Liu, H., Wu, P., & Wang, C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.022 · Cited by 24 · see citing works
Analytic Expressions for Probabilistic Moments of PL-DNN with Gaussian Input
Bibi, A., Alfadly, M., & Ghanem, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00948 · Cited by 26 · see citing works
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_2 · Cited by 24 · see citing works
Understanding classifier errors by examining influential neighbors
Kabra, M., Robie, A. A., & Branson, K. M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299017 · Cited by 23 · see citing works
Osteoporosis prescreening using dental panoramic radiographs feature analysis
Bo, C., Liang, X., Chu, P., Xu, J., Wang, D., Yang, J., Megalooikonomou, V., & Ling, H. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950498 · Cited by 22 · see citing works
Feature Learning and Deep Learning Architecture Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22 · see citing works
D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos
Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., & Zhou, B. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.09.040 · Cited by 22 · see citing works
A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm
Mahmoud, M. A. B., & Guo, P. (2019) · IEEE Access · doi:10.1109/access.2019.2919125 · Cited by 22 · see citing works
Inferring Unseen Views of People
Chen, C.-Y., & Grauman, K. L. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.258 · Cited by 21 · see citing works
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification
Shifat‐E‐Rabbi, M., Yin, X., Rubaiyat, A. H. M., Li, S., Kolouri, S., Aldroubi, A., Nichols, J. M., & Rohde, G. K. (2021) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-021-01052-0 · Cited by 21 · see citing works
Tapia, J. E., & Busch, C. (2021) · IEEE Access · doi:10.1109/access.2021.3136485 · Cited by 21 · see citing works
- Authors
- Vondrick, C., Khosla, A., Malisiewicz, T., & Torralba, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 1-8
- OpenAlex
- W1982428585
- Cited by
- 282
- References
- 25
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 9 · 2022: 5 · 2021: 12 · 2020: 14 · 2019: 23 · 2018: 47 · 2017: 33 · 2016: 49 · 2015: 43 · 2014: 37 · 2013: 1
- FWCI
- 27.65
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation (0747120)
- Multidisciplinary University Research Initiative (N00014-10-1-0933)
- Office of Naval Research (N00014-10-1-0933, MURI N00014-10-1-0933)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, feature visualization, histogram of oriented gradients, feature space visualization, false positives, augmented reality visualization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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