Title Method Affiliation Contributors Description Date

FG Detection, FG Tiling Bonn_FGT_Segm Univeristy of Bonn João Carreira, Adrian Ion, Fuxin Li, Cristian Sminchisescu - Detection on figure-ground segmentations; - Classification by detection; - Semantic segmentation on figure-ground tilings; 2010-08-30 23:08:18

NLPR_VSTAR_DET_4 Boosted HOG-LBP and multi-context (LC, EGC, HLC) National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences tntgroup Deformable model with Boosted HOG-LBP and multi-context information, use location context, enhanced global context, HOG and LBP inter-class context. 2010-08-29 15:26:53

Synthetic Training of Deformable Part Models CMIC_SynthTrain Cairo Microsoft Innovation Lab, Microsoft Research Osama Khalil, Yasmine Badr, Motaz El-Saban This submission applies synthetic training for Deformable Part Models. Using the segmentation mask of the objects, we synthesized new training examples, by relocating the objects to different background. The idea was applied on top of the deformable models approach [1]. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009. 2010-08-30 21:16:16

Deformable part models with variable sized parts CMIC_VarParts Cairo Microsoft Innovation Lab, Microsoft Research Osama Khalil, Yasmine Badr, Motaz El-Saban Our submission is based on the Deformable Part Models approach[1]. We allowed the model parts to have variable sizes accommodating for affine distortion that renders part sizes non-proportional. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009. 2010-08-30 21:15:32

parts based model and spatial pyramid features DPM-SP University of California, Irvine Yi Yang, Charless Fowlkes Parts model results rescored by combining with spatial pyramid based scene (global) classification results. Scene trained using svm with hist intersect kernel, rescoring trained using SVM on train+val+some_from_segmentations with parameters obtained via search. 2010-08-30 22:31:10

Detection Monkey Detection Monkey University of Amsterdam Koen van de Sande Jasper Uijlings Theo Gevers Arnold Smeulders The detection monkey is trained with SVM, dense Color SIFT, spatial pyramid and multiple iterations. 2010-08-30 20:46:02

Fisher with FLAIR Fisher with FLAIR University of Amsterdam Koen van de Sande, Cees Snoek, Arnold Smeulders Run for our CVPR2014 paper "Fisher and VLAD with FLAIR", see http://koen.me/research/flair 2014-06-17 11:41:40

Localisation with grouping window selection GroupLoc University of Amsterdam Jasper Uijlings Koen van de Sande Theo Gevers Arnold Smeulders Remko Scha Candidate windows are selected using hierarchical grouping. Classification is with SIFT, SVM-Histogram Intersection, Spatial Pyramid 2010-08-30 21:58:30

HOG+LBP+LTP+PLS2ROOTS HOG+LBP+LTP+PLS2ROOTS LJK,INPG Sibt ul Hussain, Bill Triggs This method consists of two roots based object detector. This detector is also trained using HOG+LBP+LTP as feature sets while PLS based linear SVM is used as learning algorithm. 2010-08-31 01:14:00

Linear svm classifier with bag of words method HOG-LBP + DHOG bag of words, SVM The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang Liblinear SVM with HOG-LBP feature and DHOG bag of words approach 2010-08-30 18:07:43

svm classifier with HOG LBP features HOG-LBP Linear SVM The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang Liblinear SVM with HOG-LBP features. All classes use the same default training parameters. 2010-08-29 07:14:40

results refined by context and classification HOGLBP_context_classification_rescore_v2 National University of Singapore Zheng Song, Qiang Chen, Shuicheng Yan Use HOG+LBP trained part-based detector. The detection results are further reranked via the context information of other detect windows and image classificaton scores. 2010-08-30 04:20:46

LSVM Mixtures of deformable part models LSVM-MDPM University of Chicago and TTI-C Pedro Felzenszwalb (UChicago), Ross Girshick (UChicago), David McAllester (TTI-C) Our submission is based on an extension of [1,2]. Each model has 6 components with 8 parts. We associate a binary mask with each component to generate segmentations. The object detection models were trained from bounding boxes. The segmentation masks were trained from segmentations. [1] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. PAMI 32(9), Sept 2010 [2] http://people.cs.uchicago.edu/~pff/latent-release4/ 2010-08-26 04:07:34

Latent hierarchical structural learning MITUCLA_Hierarchy MIT and UCLA Long Zhu, Yuanhao Chen, William Freeman, Alan Yuille, Antonio Torralba Latent hierarchical structural learning with dense HOG and HOW(SIFT) features. 2010-08-30 20:58:06

Unified Object Detection and Semantic Segmentation NUS_UDS NUS Jian Dong, Qiang Chen, Shuicheng Yan, Alan Yuille Motivated by the complementary effect observed from the typical failure cases of object detection and semantic segmentation, we propose a uni?ed framework for joint object detection and semantic segmentation [1]. By enforcing the consistency between final detection and segmentation results, our unified framework can effectively leverage the advantages of leading techniques for these two tasks. Furthermore, both local and global context information are integrated into the framework to better distinguish the ambiguous samples. By jointly optimizing the model parameters for all the components, the relative importance of different component is automatically learned for each category to guarantee the overall performance. [1] Jian Dong, Qiang Chen, Shuicheng Yan, Alan Yuille: Towards Unified Object Detection and Semantic Segmentation. ECCV 2014 2014-10-29 15:57:03

Randomized Max-Margin Compositions RM^2C University of Heidelberg, IWR/HCI Angela Eigenstetter, Masato Takami, Björn Ommer We are grouping a large number of randomly sampled parts into fewer, overlapping compositions that are trained using a maximum-margin approach. For more details see our CVPR 2014 Paper "Randomized Max-Margin Compositions for Visual Recognition". Parts are available for download on our project page http://hci.iwr.uni-heidelberg.de/COMPVIS/research/RM2C/ . 2013-10-29 08:25:28

Unsupervised Parts-based Attributes RandomParts Carnegie Mellon University Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR) http://www.cs.cmu.edu/~santosh/finalReport.pdf (unpublished work) 2010-08-25 21:50:35

Unsupervised Parts-based Attributes (max score) RandomParts_maxScore Carnegie Mellon University Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR) Updated version of earlier submission (http://www.cs.cmu.edu/~santosh/finalReport.pdf). Main update: inclusion of max score feature and one round of iterative training 2010-08-30 23:10:27

Multiple kernel learning with SIFT GMMs SIFT-GMM-MKL Tokyo Institute of Technology Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda We use multiple kernel learning and GMM supervector kernels with SIFT features. 2010-08-30 04:39:41

Multiple kernel learning with SIFT GMMs SIFT-GMM-MKL2 Tokyo Institute of Technology Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda Same as the SIFT-GMM-MKL run but the GrabCut is applied for detection. 2010-08-30 07:35:56

Svr-Segm Svr-Segm University of Bonn Joao Carreira, Fuxin Li, Adrian Ion, Cristian Sminchisescu Support vector regression to multiple descriptors extracted on segmentations. Descriptors include SIFT, color SIFT and HOG on foreground and background. Post-processing to eliminate spurious detections and segmentations. The winning method of 2009 challenge. 2010-08-30 22:52:55

Combination of Generative Discriminative Methods UC3M_Generative_Discriminative Universidad Carlos III de Madrid Iván González-Díaz, Fernando Díaz de María Combination of Supervised Topic Models with SVM-based discriminative methods for concurrent image recognition and segmentation 2010-08-30 13:27:41

LSVM mixtures of deformable part models UOCTTI_LSVM_MDPM University of Chicago Ross Girshick voc-release5 with extra octave and star-cascade 2012-05-21 05:14:06

LSVM mixtures of deformable part models UOCTTI_LSVM_MDPM University of Chicago Ross Girshick voc-release5 2012-05-11 05:14:49

LSVM Mixtures of deformable part models UOCTTI_LSVM_MDPM University of Chicago Ross Girshick voc-release5 without context 2012-05-21 17:39:28

yolov3_pytorch YOLOv3 szu yuanliang xie yishuang zhu just for a test? 2020-01-05 07:42:44

yolov3_pytorch YOLOv3 szu yuanliang xie yunbiao li image_size:544x544 have not pull on the github maybe in feature github url:https://github.com/yuanliangxie 2020-01-10 09:45:21