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Action Classification Results: VOC2010 BETA
Competition "comp9" (train on VOC2010 data)
This leaderboard shows only those submissions that have been marked as public, and so the displayed rankings should not be considered as definitive.- The highest scoring entry in each column is shown in bold.
- Clicking on the blue arrow symbol (
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the submissions from high to low wrt performance on that column.
Average Precision (AP %)
| mean | phoning | playing instrument | reading | riding bike | riding horse | running | taking photo | using computer | walking | submission date | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OppponentHOP [?] | 73.2 | 59.6 | 54.2 | 42.0 | 84.9 | 90.4 | 83.2 | 37.1 | 70.6 | 76.1 | 23-Jun-2013 | |
| Multikernel+KDA [?] | 68.3 | 52.6 | 53.5 | 35.9 | 81.0 | 89.3 | 86.5 | 32.8 | 59.2 | 68.7 | 30-Aug-2010 | |
| SVM-DOSP-MULTFEATS [?] | 67.2 | 47.0 | 57.8 | 26.9 | 78.8 | 89.7 | 87.3 | 32.5 | 60.0 | 70.1 | 30-Aug-2010 | |
| CVC-BASE [?] | 66.9 | 56.2 | 56.5 | 34.7 | 75.1 | 83.6 | 86.5 | 25.4 | 60.0 | 69.2 | 29-Aug-2010 | |
| CVC-SEL [?] | 66.4 | 49.8 | 52.8 | 34.3 | 74.2 | 85.5 | 85.1 | 25.0 | 64.1 | 72.5 | 30-Aug-2010 | |
| SPM+HT [?] | 66.1 | 53.2 | 53.6 | 30.2 | 78.2 | 88.4 | 84.6 | 30.4 | 60.9 | 61.9 | 29-Aug-2010 | |
| SVM_WHGO_SIFT_CENTRIST_low-level modeling [?] | 62.3 | 47.2 | 47.9 | 24.5 | 74.2 | 81.0 | 79.5 | 24.9 | 58.6 | 71.5 | 23-Aug-2010 | |
| dhog-ksvm [?] | 59.7 | 53.5 | 43.0 | 32.0 | 67.9 | 68.8 | 83.0 | 34.1 | 45.9 | 60.4 | 30-Aug-2010 | |
| a * SVM-SIFT + (1-a) * LSVM [?] | 58.2 | 49.2 | 37.7 | 22.2 | 73.2 | 77.1 | 81.7 | 24.3 | 53.7 | 56.9 | 29-Aug-2010 | |
| SVM-SIFT [?] | 52.9 | 47.9 | 29.1 | 21.7 | 53.5 | 76.7 | 78.3 | 26.0 | 42.9 | 56.4 | 29-Aug-2010 | |
| LSVM [?] | 48.5 | 40.4 | 29.9 | 32.2 | 53.5 | 62.2 | 73.6 | 17.6 | 45.8 | 41.5 | 29-Aug-2010 |
Abbreviations
| Title | Method | Affiliation | Contributors | Description | Date |
|---|---|---|---|---|---|
| SVM classifier with multiple features | CVC-BASE | Computer Vision Center, Universitat Autonoma de Barcelona, Spain | Nataliya Shapovalova, Wenjuan Gong, Fahad Shahbaz Khan, Josep M. Gonfaus, Marco Pedersoli, Andrew D. Bagdanov, Joost van de Weijer, Jordi González | Baseline CVC submission for action recognition. Standard BoW model over multiple features including PHOG, grayscale SIFT and (various) color SIFT descriptors. Foreground/background modeled separately, spatial pyramid over several features for foreground representation. Late fusion of feature-specific SVM outputs for final action score. | 2010-08-29 18:01:14 |
| SVM classifier with per-class feature selection | CVC-SEL | Computer Vision Center, Universitat Autonoma de Barcelona, Spain | Nataliya Shapovalova, Wenjuan Gong, Fahad Shahbaz Khan, Josep M. Gonfaus, Marco Pedersoli, Andrew D. Bagdanov, Joost van de Weijer, Jordi González | Enhanced CVC submission built upon CVC-BASE for action recognition. Standard BoW model over multiple features from CVC-BASE plus contextual object descriptors. Cross-validation procedure for action-specific feature and kernel selection. Foreground/background/neighborhood modeled separately, spatial pyramid over several features for foreground representation. Object detection based on deformable part-based detector incorporated. Late fusion of feature-specific SVM outputs for final action score. | 2010-08-30 21:16:14 |
| Felzenszwalb's part-based model. | LSVM | France, INRIA - Willow Project | Vincent Delaitre, Ivan Laptev, Josef Sivic | Felzenszwalb's part-based model trained on full train+val set with default parameters. | 2010-08-29 13:57:58 |
| Mulitkernel fusion with KDA | Multikernel+KDA | The University of Surrey | Piotr Koniusz, Muhammad Atif Tahir, Mark Barnard, Fei Yan, Krystian Mikolajczyk | Kernel-level fusion with Spatial Pyramid Grids, Soft Assignment and Kernel Discriminant Analysis using spectral regression. 18 kernels have been generated from 18 variants of SIFT. | 2010-08-30 01:48:44 |
| Opponent late fusion. | OppponentHOP | University of Surrey | Piotr Koniusz | The opponent SIFT is extracted on the dense grid and combined with Spatial Coordinate Coding, then Sparse Coding is applied. Advanced Pooling and Statistics are extracted from the features. Kernel Discriminant Classifier is applied on the linear kernel. | 2013-06-23 19:52:21 |
| Spatial Pyramids and Hough Transform | SPM+HT | INRIA | Norberto Adrián Goussies, Arnau Ramisa, Cordelia Schmid | Spatial Pyramids on the bounding box, on the image and a hough transform for taking into account the object-person interactions for bicycle, horse and tvmonitor. Trained on trainval with 5-fold cross-validation. | 2010-08-29 02:53:04 |
| SVM & dense saptial pyramid w/ multiple features | SVM-DOSP-MULTFEATS | University of Caen GREYC and INRIA LEAR | Gaurav Sharma, Frederic Jurie, Cordelia Schmid | Multiple chi squared kernels are computed: spatial pyramid (SP) w/ dense SIFT, dense overlapping SP w/ HOG, texture filter, LAB values (bag-of-words w/ the above features) and edge dir hists. They are computed on full images, person bounding boxes (BB) and BB of the lower part (simple stretch-scale of person BB) expected to contain horse, bike etc. They are combined with class specific binary weights based on their perf on val set. Finally, class specific SVMs trained on train+val. | 2010-08-30 16:21:39 |
| SVM classifier with dense SIFT features. | SVM-SIFT | France, INRIA - Willow Project | Vincent Delaitre, Ivan Laptev, Josef Sivic | SVM-Light classifier with dense SIFT features. Trained using 5-fold cross-validation. Re-trained on full train+val set with fixed parameters | 2010-08-29 13:47:55 |
| SVM classifier on low-level modeling based approac | SVM_WHGO_SIFT_CENTRIST_low-level modeling | Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology | Li Zhou, Zongtan Zhou, Dewen Hu | This method is based on a low-level modeling strategy. The approach works by creating multiple resolution images and partitioning them into sub-regions at different scales. We represent each sub-region with WHGO, CENTRIST and SIFT descriptors and combine the features of different descriptor and resolution channels through an SVM classifier to form the final decision function. | 2010-08-23 06:02:40 |
| Combination of SVM and DPM with learned weights. | a * SVM-SIFT + (1-a) * LSVM | France, INRIA - Willow Project | Vincent Delaitre, Ivan Laptev, Josef Sivic | Combination of a SVM classifier(with dense SIFT features, trained using 5-fold cross-validation and re-trained on full train+val set with fixed parameters) and of the Felzenszwalb's deformable part-based model (trained on full train+val set with default parameters).The classification score is obtained by a linear combination of the scores of the two classifiers:the two classifiers are also trained on the train set and the weights of this combination are determined by optimizing over the val set. | 2010-08-29 14:15:02 |
| kernel svm classifier with dhog feature | dhog-ksvm | University of Missouri - Columbia | Xutao Lv, Xiaoyu Wang, Xi Zhou, Tony X. Han | train SVM model with different kernels on dhog feature. | 2010-08-30 22:08:53 |