Improving the Fisher Kernel for Large-Scale Image Classification

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works

Selective Search for Object Recognition

Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

Action Recognition with Improved Trajectories

Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407 · see citing works

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works

ActivityNet: A large-scale video benchmark for human activity understanding

Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578 · see citing works

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504 · see citing works

Describing Textures in the Wild

Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.461 · Cited by 2,438 · see citing works

Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498 · see citing works

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444 · see citing works

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047 · see citing works

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298668 · Cited by 886 · see citing works

The devil is in the details: an evaluation of recent feature encoding methods

Chatfield, K., Lempitsky, V., Vedaldi, A., & Zisserman, A. (2011) · Procedings of the British Machine Vision Conference 2011 · doi:10.5244/c.25.76 · Cited by 834 · see citing works

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644 · see citing works

Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

Huang, B., Zhao, B., & Song, Y. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.050 · Cited by 591 · see citing works

PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval

Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571 · see citing works

The Computational Limits of Deep Learning

Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020) · arXiv · doi:10.48550/arxiv.2007.05558 · Cited by 538 · see citing works

Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost

Mensink, T., Verbeek, J., Perronnin, F., & Csurka, G. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_35 · Cited by 281 · see citing works

High-dimensional signature compression for large-scale image classification

Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works

Weakly Supervised Action Learning with RNN Based Fine-to-Coarse Modeling

Richard, A., Kuehne, H., & Gall, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.140 · Cited by 195 · see citing works

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175 · see citing works

BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights

Benhammou, Y., Achchab, B., Herrera, F., & Tabik, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.044 · Cited by 170 · see citing works

Event Retrieval in Large Video Collections with Circulant Temporal Encoding

Revaud, J., Douze, M., Schmid, C., & Jegou, H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.318 · Cited by 133 · see citing works

Monocular SLAM Supported Object Recognition

Pillai, S., & Leonard, J. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.034 · Cited by 118 · see citing works

A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition

Yu, Z., Tan, E.-L., Ni, D., Qin, J., Chen, S., Li, S., Lei, B., & Wang, T. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705031 · Cited by 109 · see citing works

Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection

Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62 · see citing works

Towards Unified Object Detection and Semantic Segmentation

Dong, J., Chen, Q., Yan, S., & Yuille, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_20 · Cited by 70 · see citing works

Weakly Supervised Deep Detection Networks

Bilen, H., & Vedaldi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.311 · Cited by 144 · see citing works

Compressive Sequential Learning for Action Similarity Labeling

Qin, J., Liu, L., Zhang, Z., Wang, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2508600 · Cited by 55 · see citing works

Learning deep features for image emotion classification

Chen, M., Zhang, L., & Allebach, J. P. (2015) · 2015 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2015.7351656 · Cited by 53 · see citing works