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Object DetectionIntroduction
Presentation by Jamyoung Koo
2019.03.20
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Contents
2
Object Detection using CNN
1. Basics
2. Recent Approach
3. In Remote Sensing
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Basics01
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Terms: Object Detection
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Terms: IoU(Intersection over Union)
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Terms: NMS(Non-Maximum Suppression)
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R-CNN: Architecture
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Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
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R-CNN: Problem
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Slow at test-time• Need to run full forward pass of CNN for each region proposal
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R-CNN: Problem
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SVMs and regressor• CNN features not updated in response to SVMs and regressors
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Fast R-CNN: Architecture
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Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE international conference on computer vision. 2015.
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Fast R-CNN: Architecture
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Solution: SVMs and regressor• Train it all at together End-to-End by change SVMs to FC layer
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Fast R-CNN: Architecture
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Solution: Slow at test-time• Share computation of convolutional layers by RoI pooling layer
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Fast R-CNN: RoI pooling layer
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Fast R-CNN: RoI pooling layer
14<Example in Image for visuality>
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Fast R-CNN: Problem
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Selective Search• Selective Search is very slow and is not trainable
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Faster R-CNN: Architecture
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Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal
networks." Advances in neural information processing systems. 2015.
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Faster R-CNN: Architecture
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<Example in Image for visuality>
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SSD: Single Shot multibox Detector
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Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vi
sion. Springer, Cham, 2016.
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FPN: Feature Pyramid Network
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Lin, Tsung-Yi, et al. "Feature pyramid networks for object detection." Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition. 2017.
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Recent Approach02
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CVPR’18: 28 papers
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Relation Networks for Object Detection
Finding Tiny Faces in the Wild With Generative Adversarial Network
An Analysis of Scale Invariance in Object Detection - SNIP
MegDet: A Large Mini-Batch Object Detector
Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation
Single-Shot Object Detection with Enriched Semantics
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Multi-Scale Location-Aware Kernel Representation for Object Detection
Geometry-Aware Scene Text Detection With Instance Transformation Network
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Pseudo Mask Augmented Object Detection
Feature Selective Networks for Object Detection
Single-Shot Refinement Neural Network for Object Detection
Learning Globally Optimized Object Detector via Policy Gradient
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Objects as context for detecting their semantic parts
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Cascade R-CNN: Delving into High Quality Object Detection
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Repulsion Loss: Detecting Pedestrians in a Crowd
…
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ECCV’18: 19 papers
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Acquisition of Localization Confidence for Accurate Object Detection
Receptive Field Block Net for Accurate and Fast Object Detection
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Deep Feature Pyramid Reconfiguration for Object Detection
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network CornerNet: Detecting Objects as Paired Keypoints
Zero-Shot Object Detection
Learning Region Features for Object Detection
Graininess-Aware Deep Feature Learning for Pedestrian Detection
DetNet: Design Backbone for Object Detection
PyramidBox: A Context-assisted Single Shot Face Detector
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Object Detection with an Aligned Spatial-Temporal Memory
Localization Recall Precision (LRP): A New Performance Metric for Object Detection Context Refinement for Object Detection
Context Refinement for Object Detection
Bi-box Regression for Pedestrian Detection and Occlusion Estimation
Deep Feature Pyramid Reconfiguration for Object Detection
Parallel Feature Pyramid Network for Object Detection
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Learning Region Features for Object Detection
Where are the Blobs: Counting by Localization with Point Supervision(Counting)Geometry-Aware Scene Text Detection With Instance Transformation Network
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SNIP
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SNIP
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Problem Definition: Large scale variation
• Why is object detection so much harder than image classification?
Large scale variation across object instances
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SNIP
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Cascade R-CNN
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Cascade R-CNN
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Accuracy
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Cascade R-CNN: Quality
28High quality detector
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Cascade R-CNN: Problem
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Overfitting during training, due to exponentially vanishing positive samples
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Cascade R-CNN: Main Idea
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Cascade R-CNN: Architecture
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IoU-Net
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IoU-Net: Problem
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IoU-Net: Main Idea
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IoU-Net
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IoU-Net
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Meta-Anchor
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Meta-Anchor: Problem
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Anchors are fixed
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Meta-Anchor: Architecutre
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In Remote Sensing03
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Oriented Objects
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Oriented Objects
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Bounding Box V.S. Rotated Bounding Box
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RBox-CNN
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RBox-CNN
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RBox-CNN
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<ICPR’18 ODAI Contest>
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R2CNN++
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R2CNN++
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Yang, Xue, et al. "R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust
Anchor Strategy." arXiv preprint arXiv:1811.07126 (2018).
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R2CNN++
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IF-Net: Inception Fusion Network
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R2CNN++
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MDANet: Multi-Dimensional Attention Network
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Learning RoI Transformer
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Learning RoI Transformer
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CAD-Net
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CAD-Net
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Appendix: CVPR Workshop
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Thank you