Fine-grained Fine-grained Recognition( 细粒度分类 ) 沈志强.

Slides:



Advertisements
Similar presentations
Attributes for Classifier Feedback Amar Parkash and Devi Parikh.
Advertisements

Rich feature Hierarchies for Accurate object detection and semantic segmentation Ross Girshick, Jeff Donahue, Trevor Darrell, Jitandra Malik (UC Berkeley)
Presenter: Duan Tran (Part of slides are from Pedro’s)
Jan-Michael Frahm, Enrique Dunn Spring 2013
Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights.
Lecture 31: Modern object recognition
Many slides based on P. FelzenszwalbP. Felzenszwalb General object detection with deformable part-based models.
Mixture of trees model: Face Detection, Pose Estimation and Landmark Localization Presenter: Zhang Li.
Steerable Part Models Hamed Pirsiavash and Deva Ramanan
Structural Human Action Recognition from Still Images Moin Nabi Computer Vision Lab. ©IPM - Oct
Intelligent Systems Lab. Recognizing Human actions from Still Images with Latent Poses Authors: Weilong Yang, Yang Wang, and Greg Mori Simon Fraser University,
Intro to DPM By Zhangliliang. Outline Intuition Introduction to DPM Model Inference(matching) Training latent SVM Training Procedure Initialization Post-processing.
Object-centric spatial pooling for image classification Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei ECCV 2012.
Enhancing Exemplar SVMs using Part Level Transfer Regularization 1.
Large-Scale Object Recognition with Weak Supervision
More sliding window detection: Discriminative part-based models Many slides based on P. FelzenszwalbP. Felzenszwalb.
Student: Yao-Sheng Wang Advisor: Prof. Sheng-Jyh Wang ARTICULATED HUMAN DETECTION 1 Department of Electronics Engineering National Chiao Tung University.
Recognition using Regions CVPR Outline Introduction Overview of the Approach Experimental Results Conclusion.
Beyond bags of features: Adding spatial information Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.
1 Learning to Detect Objects in Images via a Sparse, Part-Based Representation S. Agarwal, A. Awan and D. Roth IEEE Transactions on Pattern Analysis and.
Beyond bags of features: Adding spatial information Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.
Object Recognizing We will discuss: Features Classifiers Example ‘winning’ system.
PANDA: Pose Aligned Networks for Deep Attribute Modeling Ning Zhang1;2, Manohar Paluri1, Marc’Aurelio Ranzato1, Trevor Darrell2, Lubomir Bourdev1 1: Facebook.
Spatial Pyramid Pooling in Deep Convolutional
Generic object detection with deformable part-based models
Object Bank Presenter : Liu Changyu Advisor : Prof. Alex Hauptmann Interest : Multimedia Analysis April 4 th, 2013.
Object Recognizing. Recognition -- topics Features Classifiers Example ‘winning’ system.
Nonparametric Part Transfer for Fine-grained Recognition Presenter Byungju Kim.
“Secret” of Object Detection Zheng Wu (Summer intern in MSRNE) Sep. 3, 2010 Joint work with Ce Liu (MSRNE) William T. Freeman (MIT) Adam Kalai (MSRNE)
Detection, Segmentation and Fine-grained Localization
Marco Pedersoli, Jordi Gonzàlez, Xu Hu, and Xavier Roca
Object Detection with Discriminatively Trained Part Based Models
Lecture 31: Modern recognition CS4670 / 5670: Computer Vision Noah Snavely.
O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang.
A Codebook-Free and Annotation-free Approach for Fine-Grained Image Categorization Authors Bangpeng Yao et al. Presenter Hyung-seok Lee ( 이형석 ) CVPR 2012.
Deformable Part Model Presenter : Liu Changyu Advisor : Prof. Alex Hauptmann Interest : Multimedia Analysis April 11 st, 2013.
Deformable Part Models (DPM) Felzenswalb, Girshick, McAllester & Ramanan (2010) Slides drawn from a tutorial By R. Girshick AP 12% 27% 36% 45% 49% 2005.
Training and Evaluating of Object Bank Models Presenter : Changyu Liu Advisor : Prof. Alex Interest : Multimedia Analysis May 16 th, 2013.
Object detection, deep learning, and R-CNNs
CS 1699: Intro to Computer Vision Detection II: Deformable Part Models Prof. Adriana Kovashka University of Pittsburgh November 12, 2015.
Object Detection Overview Viola-Jones Dalal-Triggs Deformable models Deep learning.
Recognition Using Visual Phrases
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.
Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations ZUO ZHEN 27 SEP 2011.
Object Recognizing. Object Classes Individual Recognition.
CS 2750: Machine Learning Support Vector Machines Prof. Adriana Kovashka University of Pittsburgh February 17, 2016.
Cascade Region Regression for Robust Object Detection
More sliding window detection: Discriminative part-based models
Object Recognizing. Object Classes Individual Recognition.
A Discriminatively Trained, Multiscale, Deformable Part Model Yeong-Jun Cho Computer Vision and Pattern Recognition,2008.
Rich feature hierarchies for accurate object detection and semantic segmentation 2014 IEEE Conference on Computer Vision and Pattern Recognition Ross Girshick,
PANDA: Pose Aligned Networks for Deep Attribute Modeling Ning Zhang 1,2 Manohar Paluri 1 Marć Aurelio Ranzato 1 Trevor Darrell 2 Lumbomir Boudev 1 1 Facebook.
1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.
Recent developments in object detection
Compact Bilinear Pooling
Object detection with deformable part-based models
Data Driven Attributes for Action Detection
Krishna Kumar Singh, Yong Jae Lee University of California, Davis
Paper Presentation: Shape and Matching
Object Localization Goal: detect the location of an object within an image Fully supervised: Training data labeled with object category and ground truth.
ICCV Hierarchical Part Matching for Fine-Grained Image Classification
R-CNN region By Ilia Iofedov 11/11/2018 BGU, DNN course 2016.
Object detection.
Computer Vision James Hays
Image Classification.
Object Detection + Deep Learning
Outline Background Motivation Proposed Model Experimental Results
RCNN, Fast-RCNN, Faster-RCNN
Human-object interaction
Presentation transcript:

Fine-grained Fine-grained Recognition( 细粒度分类 ) 沈志强

Datasets -- Caltech-UCSD Bird Number of categories: 200 Number of images: 11,788 Annotations per image: 15 Part Locations, 1 Bounding Box

Methods feature extraction + classification global feature extraction + part feature representations

Object hypothesis [1] Multiscale model: the resolution of part filters is twice the resolution of the root

Scoring an object hypothesis The score of a hypothesis is the sum of filter scores minus the sum of deformation costs Filters Subwindow features Deformation weights Displacements

Scoring an object hypothesis The score of a hypothesis is the sum of filter scores minus the sum of deformation costs Concatenation of filter and deformation weights Concatenation of subwindow features and displacements Filters Subwindow features Deformation weights Displacements

Training Our classifier has the form w are model parameters, z are latent hypotheses Latent SVM training: Initialize w and iterate: Fix w and find the best z for each training example (detection) Fix z and solve for w (standard SVM training) Issue: too many negative examples Do “data mining” to find “hard” negatives

Deformable Part Descriptors (DPDs) - ICCV2013 [4] Strongly-supervised DPD Weakly-supervised DPD

Pose-normalization Strongly-supervised DPD is the pooled image feature for semantic region r l figure out a mapping S (j) :

Pose-normalization Weakly-supervised DPD

Detection results

Nonparametric Part Transfer for Fine-grained Recognition(CVPR 2014) [3]

Nonparametric Part Transfer for Fine-grained Recognition(CVPR 2014)

The distribution is clearly non-Gaussian, therefore, a single DPM model would not be able to model the variation present in the training dataset.

Nonparametric Part Transfer for Fine-grained Recognition(CVPR 2014)

Example detections

Part-based R-CNNs for Fine-grained Category Detection(ECCV 2014 oral) [2]

Part-based R-CNNs for Fine-grained Category Detection(ECCV 2014 oral) Geometric constraints Let X = {x 0, x 1,..., x n } denote the locations (bounding boxes) of object p0 and n parts {p i }. where σ (·) is the sigmoid function and φ (x) is the CNN feature descriptor extracted at location x. where ∆(X) defines a scoring function over the joint configuration of the object and root bounding box.

Part-based R-CNNs for Fine-grained Category Detection(ECCV 2014 oral) Box constraints

Part-based R-CNNs for Fine-grained Category Detection(ECCV 2014 oral) Geometric constraints where δ i is a scoring function for the position of the part p i given the training data.

Illustration of geometric constant

Recall

Results

Conclusion feature extraction + classification global feature extraction and part feature representations Part localization is a crucial step.

References [1] Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D. Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010) [2] Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell.Part-based R- CNNs for Fine-grained Category Detection. ECCV [3] Christoph Goring, Erik Rodner, Alexander Freytag, and Joachim Denzler ∗. Nonparametric Part Transfer for Fine-grained Recognition. CVPR 2014 [4] N. Zhang, R. Farrell, F. Iandola, and T. Darrell. Deformable part descriptors for fine-grained recognition and attribute prediction. In ICCV, 2013.

Thanks & Questions