Image Classification with the Fisher Vector: Theory and Practice

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Distinctive Image Features from Scale-Invariant Keypoints

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The pascal visual object classes (VOC) challenge

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

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The Pascal Visual Object Classes Challenge: A Retrospective

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Video Google: a text retrieval approach to object matching in videos

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Visual categorization with bags of keypoints

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SUN database: Large-scale scene recognition from abbey to zoo

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Locality-constrained Linear Coding for image classification

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Squeeze-and-Excitation Networks

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Squeeze-and-Excitation Networks

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

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Multi-view Convolutional Neural Networks for 3D Shape Recognition

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Food-101 – Mining Discriminative Components with Random Forests

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Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036

Second-Order Attention Network for Single Image Super-Resolution

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Sketch-based manga retrieval using manga109 dataset

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Action recognition with trajectory-pooled deep-convolutional descriptors

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Multi-scale Orderless Pooling of Deep Convolutional Activation Features

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Unmasking Clever Hans predictors and assessing what machines really learn

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MARS: A Video Benchmark for Large-Scale Person Re-Identification

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Temporal Segment Networks for Action Recognition in Videos

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Label-Embedding for Image Classification

Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2487986 · Cited by 787

Aggregating Local Deep Features for Image Retrieval

Yandex, A. B., & Lempitsky, V. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.150 · Cited by 679

Human Action Recognition From Various Data Modalities: A Review

Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 719

Towards Explainable Artificial Intelligence

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The THUMOS challenge on action recognition for videos “in the wild”

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Gather-Excite: Exploiting Feature Context in Convolutional Neural NetworksPreprint

Hu, J., Shen, L., Albanie, S., Sun, G., & Vedaldi, A. (2018) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1810.12348 · Cited by 593

Hierarchical Gaussian Descriptor for Person Re-identification

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Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580

Word Spotting and Recognition with Embedded Attributes

Almazán, J., Gordo, A., Fornés, A., & Valveny, E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2339814 · Cited by 514

Revisiting multiple instance neural networks

Wang, X., Yan, Y., Tang, P., Bai, X., & Liu, W. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.026 · Cited by 506

Fine-Grained Image Analysis With Deep Learning: A Survey

Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126648 · Cited by 526

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Wei, X.-S., Luo, J.-H., Wu, J., & Zhou, Z. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2688133 · Cited by 462

Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420

Real-Time Action Recognition with Enhanced Motion Vector CNNs

Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (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.2016.297 · Cited by 444

A discriminative CNN video representation for event detection

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Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning

Pan, P., Xu, Z., Yang, Y., Wu, F. Q., & Zhuang, Y. (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.2016.117 · Cited by 420

RGB-D-based human motion recognition with deep learning: A survey

Wang, P., Li, W., Ogunbona, P., Wan, J., & Escalera, S. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.04.007 · Cited by 412

The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding

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Overview of Environment Perception for Intelligent Vehicles

Zhu, H., Yuen, K., Mihaylova, L. S., & Leung, H. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2658662 · Cited by 386

Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

Jiang, Y., Wu, Z., Wang, J., Xue, X., & Chang, S. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2670560 · Cited by 411

Action and Event Recognition with Fisher Vectors on a Compact Feature Set

Oneaţă, D., Verbeek, J. J., & Schmid, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.228 · Cited by 375

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

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Local binary features for texture classification: Taxonomy and experimental study

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Large-Scale Object Classification Using Label Relation Graphs

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Do We Really Need to Collect Millions of Faces for Effective Face Recognition?

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Towards Good Practices for Very Deep Two-Stream ConvNetsPreprint

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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

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SUN Database: Exploring a Large Collection of Scene Categories

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Sparse Modeling for Image and Vision Processing

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Semantic Pooling for Complex Event Analysis in Untrimmed Videos

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Skeleton-Based Dynamic Hand Gesture Recognition

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A Robust and Efficient Video Representation for Action Recognition

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Sparse Modeling for Image and Vision Processing

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HICO: A Benchmark for Recognizing Human-Object Interactions in Images

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Is Second-Order Information Helpful for Large-Scale Visual Recognition?

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An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

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Associating neural word embeddings with deep image representations using Fisher Vectors

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Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM

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Delving deep into spatial pooling for squeeze-and-excitation networks

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Skeletal Quads: Human Action Recognition Using Joint Quadruples

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Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

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A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., & Radke, R. J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2807450 · Cited by 315

Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features

Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., Lei, B., & Wang, T. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2866166 · Cited by 253

Generalized Max Pooling

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Pose-Aware Face Recognition in the Wild

Masi, I., Rawls, S., Medioni, G. G., & Natarajan, P. (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.2016.523 · Cited by 242

Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection

Zolfaghari, M., Oliveira, G. L., Sedaghat, N., & Brox, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.316 · Cited by 248

3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks

Ben-Shabat, Y., Lindenbaum, M., & Fischer, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2850061 · Cited by 237

Wide-Slice Residual Networks for Food Recognition

Martinel, N., Foresti, G. L., & Micheloni, C. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00068 · Cited by 233

Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition

Wu, L. Y., Wang, Y., Li, X., & Gao, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2813971 · Cited by 234

Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification

Wu, L. Y., Wang, Y., Gao, J., & Li, X. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2877886 · Cited by 232

Timeception for Complex Action Recognition

Hussein, N., Gavves, E., & Smeulders, A. W. M. (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.2019.00034 · Cited by 228

Multi-fold MIL Training for Weakly Supervised Object Localization

Cinbiş, R. G., Verbeek, J. J., & Schmid, C. (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.2014.309 · Cited by 219

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

Gao, Z., Wang, L., Zhou, L., & Zhang, J. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2526603 · Cited by 223

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Singla, A., Yuan, L., & Ebrahimi, T. (2016) · International Workshop on Multimedia Assisted Dietary Management (MADiMa) · doi:10.1145/2986035.2986039 · Cited by 220

A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer

Barata, C., Celebi, M. E., & Marques, J. S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2845939 · Cited by 218

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Lapuschkin, S., Binder, A., Montavon, G., Müller, K., & Samek, W. (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.2016.318 · Cited by 226

Deep Learning for Instance Retrieval: A Survey

Chen, W., Liu, Y., Wang, W., Bakker, E. M., Georgiou, T., Fieguth, P., Liu, L., & Lew, M. S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218591 · Cited by 282

Task2Vec: Task Embedding for Meta-Learning

Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C. C., Soatto, S., & Perona, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00653 · Cited by 210

A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification

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The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

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Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification

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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.191 · Cited by 196

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

COSTA: Co-Occurrence Statistics for Zero-Shot Classification

Mensink, T., Gavves, E., & Snoek, C. G. M. (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.2014.313 · Cited by 194

Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation

Zhang, Y., Wei, X.-S., Wu, J., Cai, J., Lu, J., Nguyen, V.-A., & Do, M. N. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2531289 · Cited by 193

Unsupervised Learning of Long-Term Motion Dynamics for Videos

Luo, Z., Peng, B., Huang, D.-A., Alahi, A., & Fei-Fei, L. (2017) · 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.2017.751 · Cited by 196

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Wang, X., Yang, W. H., Weinreb, J. C., Han, J., Li, Q., Kong, X., Yan, Y., Ke, Z., Luo, B., Liu, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-15720-y · Cited by 200

Multi-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 193

Spatio-temporal Object Detection Proposals

Oneaţă, D., Revaud, J., Verbeek, J. J., & Schmid, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_48 · Cited by 184

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Afifi, M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7424-8 · Cited by 194

Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication

Bucher, M., Herbin, S., & Jurie, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_44 · Cited by 197

An end-to-end generative framework for video segmentation and recognition

Kuehne, H., Gall, J., & Serre, T. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477701 · Cited by 188

Exploit Bounding Box Annotations for Multi-Label Object Recognition

Yang, H., Zhou, J. T., Zhang, Y., Gao, B.-B., Wu, J., & Cai, J. (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.2016.37 · Cited by 188

Cross-Modality Bridging and Knowledge Transferring for Image Understanding

Yan, C. C., Li, L., Zhang, C., Liu, B., Zhang, Y., & Dai, Q. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2903448 · Cited by 182

Convolutional recurrent neural networks: Learning spatial dependencies for image representation

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Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs

Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2791180 · Cited by 178

Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering

Zhou, G., He, T., Zhao, J., & Hu, P. (2015) · doi:10.3115/v1/p15-1025 · Cited by 177

Convolutional neural network-based image representation for visual loop closure detection

Hou, Y., Zhang, H., & Zhou, S. (2015) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2015.7279659 · Cited by 173

Joint embeddings of shapes and images via CNN image purification

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International Journal of Computer Vision manuscript No. (will be inserted by the editor) Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. J. (2014)

and Practice

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