CAP 6412 Advanced Computer Vision, Spring 2020 Consistency-based Semi-supervised learning for Object Detection Authors: Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak Venue: Advances in Neural Information Processing Systems 32 (NIPS 2019) Presenter: Ishan Dave 1
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Consistency-based Semi-supervised learning for Object ...Type of object detector: quick review Single stage Obj Detector Two stage Obj Detector Region Proposal Network Classifier Eg.
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CAP 6412 Advanced Computer Vision, Spring 2020
Consistency-based Semi-supervised learning for Object Detection
Authors: Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak
Venue: Advances in Neural Information Processing Systems 32 (NIPS 2019)
Presenter: Ishan Dave
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Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Outline of this presentation
● Understanding the problem and terminologies● Existing Approaches to solve the problem● Inspiration of the proposed work● Proposed method● Technical Contribution● Results● Conclusion
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Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Understanding the problem and terminologies: Object Detector
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Type of object detector: quick reviewSingle stage Obj Detector
Two stage Obj Detector
Region Proposal Network Classifier
Eg. SSD, YOLO
Eg. Faster RCNN
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Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE international conference on computer vision. 2015.
Understanding the Problem
Existing methods
Inspiration of the work
Proposed Method
Technical Contribution Results
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Understanding the problem and terminologies: Motivation
Supervised LearningObject
DetectorModel
Classification and Localization Loss
Semi-Supervised Learning
Object DetectorModel
Classification and Localization Loss for
labeled data
But what losses for Unlabled data?
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Understanding the Problem
Existing methods
Inspiration of the work
Proposed Method
Technical Contribution Results
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Any questions till this point?
✓ Understanding the problem and terminologies✓ Object Detector✓ Different Level of Supervision✓ Motivation
❏ Existing Approaches to solve the problem
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Understanding the Problem
Existing methods
Inspiration of the work
Proposed Method
Technical Contribution Results
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
A common approach in Semi-supervised learning
Object DetectorModel
Classification and Localization Loss
TRAINEDObject
DetectorModel
Labeled data
Prediction“Horse” at [245, 240, 50, 50] with 0.93 confidence
Confidence threshold
Inference Mode
Training Mode
“Pseudo Labels”
“Self-Training approach”
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Understanding the Problem
Existing methods
Inspiration of the work
Proposed Method
Technical Contribution Results
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020
Consistency regularization
Classifier Model
Original (x)
Perturbed (x’)
Loss
f(x)
f(x’)
State-of-the-Art in Semi-Supervised Classification!
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Understanding the Problem
Existing methods
Inspiration of the work
Proposed Method
Technical Contribution Results
Consistency-based Semi-supervised Learning for Object Detection (NIPS-2019)CAP 6412 Advanced Computer Vision, Spring 2020