PR-252: Making Convolutional Networks Shift-Invariant Again

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이번 논문은 Convolutional Neural Network에서 발생하는 Aliasing 문제를 지적하고, 이를 고전적인 신호처리 기법을 이용하여 해결하는 논문입니다. Paper Link: https://arxiv.org/abs/1904.11486 Youtube Link: https://youtu.be/oTIBFH6M7YM

Making Convolutional Networks
Shift-Invariant Again
Hyeongmin Lee
Image and Video Pattern Recognition LAB
Electrical and Electronic Engineering Dept, Yonsei University
5th Semester
PR-252

What is shift-invariancy??
 Shift-variant??
Change in performance??

What is shift-invariancy??
 Shift-variant??

Aliasing
 Fourier Transform & Frequency Domain
𝑡𝑡 Ω
𝑋𝑋 Ω = �
−∞
∞
𝑥𝑥 𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡 𝑑𝑑𝑑𝑑

Aliasing
 Down-sampling in frequency domain
𝑇𝑇 𝑥𝑥 𝑡𝑡 = �
−∞
∞
𝑥𝑥 𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡 𝑑𝑑𝑑𝑑 = 𝑋𝑋(Ω)
𝑇𝑇 𝑥𝑥 𝑎𝑎𝑡𝑡 = �
−∞
∞
𝑥𝑥 𝑎𝑎𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡
𝑑𝑑𝑑𝑑 =
1
|𝑎𝑎|
�
−∞
∞
𝑥𝑥 𝑡𝑡′ 𝑒𝑒−𝑗𝑗
Ω
𝑎𝑎
𝑡𝑡′
𝑑𝑑𝑑𝑑′
𝑇𝑇 𝑥𝑥 𝑎𝑎𝑡𝑡 =
1
|𝑎𝑎|
𝑋𝑋(Ω/𝑎𝑎)

Aliasing
 Down-sampling in frequency domain
𝑡𝑡 Ω
𝑡𝑡 Ω

Aliasing
 Discrete Signal in Frequency Domain (Discrete Time Fourier Transform)
𝜔𝜔𝑛𝑛 2𝜋𝜋−2𝜋𝜋
𝑋𝑋 𝜔𝜔 = �
𝑛𝑛=−∞
∞
𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗
𝑋𝑋 𝜔𝜔 + 2𝜋𝜋 = �
𝑛𝑛=−∞
∞
𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗𝑗 = �
𝑛𝑛=−∞
∞
𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗 = 𝑋𝑋(𝜔𝜔)

Aliasing
 Discrete Down-Sampling in Frequency Domain
𝑓𝑓
𝑓𝑓
𝑛𝑛 2𝜋𝜋−2𝜋𝜋
𝑛𝑛
2𝜋𝜋−2𝜋𝜋
Aliasing Aliasing

Aliasing in CNN
 Max pooling
 Average pooling
 Strided convolution
0 0 1 1 0 0 1 1 0 1 0 1
0 1 1 0 0 1 1 0 1 1 1 1
Max-pooling
Max-pooling

Anti-Aliasing
 Shift Invariancy & Shift Equivariance
• Shift Equivariance
• Shift Invariancy
Shift-Equivariant  Shift-Invariant

Anti-Aliasing
 Anti-aliasing
𝑓𝑓2𝜋𝜋−2𝜋𝜋
Low Pass Filtering
(Blurring)
𝑓𝑓2𝜋𝜋−2𝜋𝜋
𝑓𝑓2𝜋𝜋−2𝜋𝜋
Sampling

Anti-Aliasing
 Anti-aliasing for max pooling
0 0 1 1 0 0 1 1 0 1 0 10 1 1 1 0 1 1 1
Max Sampling
0 1 1 0 0 1 1 0 1 1 1 11 1 1 0 1 1 1 0
Shift-Equivariant!!

Anti-Aliasing
 Anti-aliasing for max pooling
0 1 1 1 0 1 1 1 0.5 1 0.5 10.5 1 1 0.5 0.5 1 1 0.5
Blurring Subsampling
1 1 1 0 1 1 1 0 1 0.5 1 0.51 1 0.5 0.5 1 1 0.5 0.5
Max

Anti-Aliasing
 Anti-aliasing for various sampling operations in CNN
• MaxPool
• AveragePool
• StrideConv

Results
 Improvement in Image Translation

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PR-455: CoTracker: It is Better to Track Together

Hyeongmin Lee

이번 영상에서는 제가 PR 278번째로 소개드린 적 있었던 RAFT의 Point Tracking 버전 논문입니다. 보통 Object Traking은 주어진 bounding box를 track하는 task를 말하는데 본 논문에서는 첫 프레임에 주어진 point를 따라가는 task를 다루고 있습니다. 논문 제목에서 이야기 하듯이, 주어진 point 하나를 따라가는 것보다 여러 point를 함께 따라가면서 서로 정보를 주고받는 등의 interaction을 하는 것이 tracking 성능 향상에 도움이 된다는 것이 이 논문의 main idea입니다. 논문 링크: https://arxiv.org/abs/2307.07635 영상 링크: https://youtu.be/BDfTSm3_hys

PR-430: CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retri...

Hyeongmin Lee

This document summarizes research on using CLIP to perform end-to-end video clip retrieval. It presents CLIP4Clip, which uses a CLIP backbone pretrained on large image-text datasets to encode video clips and text queries into a shared embedding space. CLIP4Clip flattens patches from a video encoder into vectors and calculates similarity between video and text embeddings for retrieval. It is trained on HowTo100M video clips and outperforms prior work on benchmark datasets like MSR-VTT, achieving state-of-the-art video clip retrieval results.

PR-420: Scalable Model Compression by Entropy Penalized Reparameterization

Hyeongmin Lee

제가 이번에 소개드릴 논문은 Scalable Model Compression by Entropy Penalized Reparameterization이라는 논문입니다. 이전에 꾸준히 Deep Learning을 이용한 이미지 및 비디오 압축에 대해 설명드렸던 바가 있는데, 이번에는 Neural Network의 Model Parameter들을 압축하는 방법에 관한 논문입니다. 논문 링크: https://arxiv.org/abs/1906.06624 영상 링크: https://youtu.be/LJ8WD5MKA2o

PR-409: Denoising Diffusion Probabilistic Models

Hyeongmin Lee

이번 논문은 요즘 핫한 Diffusion을 처음으로 유행시킨 Denoising Diffusion Probabilistic Models (DDPM) 입니다. ICML 2015년에 처음 제안된 Diffusion의 여러 실용적인 측면들을 멋지게 해결하여 그 유행의 시작을 알린 논문인데요, Generative Model의 여러 분야와 Diffusion, 그리고 DDPM에서는 무엇이 바뀌었는지 알아보도록 하겠습니다. 논문 링크: https://arxiv.org/abs/2006.11239 영상 링크: https://youtu.be/1j0W_lu55nc

PR-395: Variational Image Compression with a Scale Hyperprior

Hyeongmin Lee

제가 이번에 소개드릴 논문은 Variational Image Compression with a Scale Hyperprior라는 논문입니다. 지난 328번째 발표에 이어서 두번째 Deep Learning-based Image Compression이고, 지난번 발표때 다루지 못했던 Variational Autoencoder와의 관계와 이번 논문에서의 새 Contribution까지, Deep Learning을 이용한 Image Compression연구는 어떤 고민을 주로 하고 있는지 등을 전달해드리고자 노력하였습니다. 논문 링크: https://arxiv.org/abs/1802.01436 영상 링크: https://youtu.be/ne9ieHRsfCc

PR-386: Light Field Networks: Neural Scene Representations with Single-Evalua...

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제가 이번에 소개드릴 논문은 NeRF와 같이 view synthesis를 하는 논문입니다. NeRF 이후로 NeRF의 문제점을 보완하기 위해 여러 방법들이 쏟아져 나왔는데요, 다른 한편으로는 발상의 전환을 통해 NeRF와 다른 방법을 활용하고자 하는 시도들도 있는 편입니다. 그러한 가장 대표적인 방법중 하나인 Neural Light Field Rendering 방식에 대해 설명드리겠습니다. 논문 링크: https://arxiv.org/abs/2106.02634 영상 링크: https://youtu.be/gxag8uvA2Sc

PR-376: Softmax Splatting for Video Frame Interpolation

Hyeongmin Lee

This document proposes a method called softmax splatting for video frame interpolation. It summarizes previous approaches like averaging frames and using optical flow. Softmax splatting uses optical flow to warp input frames and applies a softmax function to interpolate pixel values, assigning higher weights to pixels with smaller displacement. This allows pixels to be interpolated from multiple locations instead of just their forward flow mapping. The method uses a neural network to estimate optical flow and perform softmax splatting for high quality frame interpolation between input video frames.

PR-365: Fast object detection in compressed video

Hyeongmin Lee

이번 PR12 365번째 논문으로 소개드릴 내용은 조금 특이한 접근법입니다. 우리가 실생활에서 접하는 대부분의 비디오는 Compressed 된 형태의 Video인데요, 실제 Computer Vision Task에서 input이 Compressed Video라는 가정을 하게 되면 생각보다 큰 이점을 얻을 수 있습니다. 바로 Compressed Video에는 Motion Vector가 포함되어있다는 점입니다. 이를 이용하면 생각보다 많은 것들을 할 수 있게 됩니다. 그 예시로 Object Detection의 연산량을 크게 줄인 case를 하나 소개드려보고자 합니다. paper link: https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Fast_Object_Detection_in_Compressed_Video_ICCV_2019_paper.html video link: https://youtu.be/9n6OtHtJvJ0

PR-340: DVC: An End-to-end Deep Video Compression Framework

Hyeongmin Lee

이번 PR12 340번째 논문으로 소개드릴 내용은 Deep Learning을 이용한 Video Compression에 관한 내용입니다. 바로 이전 논문으로 Deep Learning을 이용한 Image Compression에 대해 설명드렸었는데요, 시간 여유가 있으신 분들께서는 이전 영상 먼저 보시고 오는 것을 추천드립니다 :) 이전 영상: https://www.youtube.com/watch?v=rtuJqQDWmIA paper link: https://arxiv.org/abs/1812.00101 youtube link: https://youtu.be/Dd8Gj2ZITkA

PR-328: End-to-End OptimizedImage Compression

Hyeongmin Lee

PR 328번째 논문은 ICLR 2017에 발표된 "End-to-End OptimizedImage Compression"이라는 논문입니다. 이미지 압축에 대해 들어보신 적이 있으신가요? 이미지를 더 적은 비트, 즉 더 적은 용량의 데이터로 표현하기 위해 다양한 압축 방법이 제안되어 왔습니다. 가장 대표적인 기술이 JPEG이라고 할 수 있겠는데요, 이 논문에서는 End-to-End Deep Learning을 이용하여 이미지를 압축하는 기법을 제안합니다. 이 논문에서 제안한 방법과 더불어 이미지 압축에 필요한 기본 개념들까지 함께 정리하였으니 이미지 압축이라는 분야가 단순히 무엇인지 궁금하신 분들께서도 앞에서부터 차근차근 봐주시면 감사드리겠습니다 :) paper link: https://arxiv.org/abs/1611.01704 youtube link: https://youtu.be/rtuJqQDWmIA

PR-315: Taming Transformers for High-Resolution Image Synthesis

Hyeongmin Lee

요즘 Transformer 구조를 language랑 vision 관계 없이 여기저기 적용해보려는 시도가 매우 다양하게 이루어지고 있는데요, 그래서 이번주 제 발표에서는 이를 High-resolution image synthesis에 활용한, CVPR 2021 Oral Session에서 발표될 논문 하나를 소개해보려고 합니다! ** 방송 기기 문제로 이번 영상은 아이패드 필기 없이 진행됩니다!! ** 논문 링크: https://arxiv.org/abs/2012.09841 영상 링크: https://youtu.be/GcbT0IGt0xE

PR-278: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

Hyeongmin Lee

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Hyeongmin Lee

이번에 다룰 논문은 "Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation"이라는 논문입니다. 얼마 전에 발표드렸던 FlowNet 논문처럼 이 논문도 Deep Learning을 통해 Optical Flow를 학습하는 방법입니다. 다른 점이 하나 있다면, Unsupervised 방식으로 학습이 진행된다는 점입니다. Supervised 방식 만큼이나 Unsupervised 방식으로 Optical Flow를 학습하는 연구 역시 이미 많이 진행이 되어 왔는데요, 오늘 소개 드릴 논문에서는 Data Augmentation을 통한 Consistency를 활용하여 성능을 높이는 방식을 채용한 경우를 소개드리고자 합니다. 영상 링크: 이번에 다룰 논문은 "Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation"이라는 논문입니다. 얼마 전에 발표드렸던 FlowNet 논문처럼 이 논문도 Deep Learning을 통해 Optical Flow를 학습하는 방법입니다. 다른 점이 하나 있다면, Unsupervised 방식으로 학습이 진행된다는 점입니다. Supervised 방식 만큼이나 Unsupervised 방식으로 Optical Flow를 학습하는 연구 역시 이미 많이 진행이 되어 왔는데요, 오늘 소개 드릴 논문에서는 Data Augmentation을 통한 Consistency를 활용하여 성능을 높이는 방식을 채용한 경우를 소개드리고자 합니다.

PR-240: Modulating Image Restoration with Continual Levels viaAdaptive Featu...

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PR-214: FlowNet: Learning Optical Flow with Convolutional Networks

Hyeongmin Lee

제 PR12 첫번째 발표 논문은 FlowNet이라는 논문입니다. Optical Flow는 비디오의 인접한 Frame에 대하여 각 Pixel이 첫 번째 Frame에서 두 번째 Frame으로 얼마나 이동했는지의 Vector를 모든 위치에 대하여 나타낸 Map입니다. Video에 Motion을 분석하는 일은 매우 중요하기 때문에, 이러한 Optical Flow 역시 굉장히 중요한 요소 중 하나인데요, 이번 영상에서는 고전적인 Computer Vision에서 쓰였던 다양한 Optical Flow 알고리즘들과, Deep Learning Based로 Optical Flow를 구하는 Neural Network인 FlowNet에 대하여 알아보겠습니다. 감사합니다!! 영상 링크: https://youtu.be/Z_t0shK98pM 논문 링크: http://openaccess.thecvf.com/content_iccv_2015/html/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.html

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PR-252: Making Convolutional Networks Shift-Invariant Again

1. Making Convolutional Networks Shift-Invariant Again Hyeongmin Lee Image and Video Pattern Recognition LAB Electrical and Electronic Engineering Dept, Yonsei University 5th Semester PR-252

3. What is shift-invariancy??

4. What is shift-invariancy??  Shift-variant?? Change in performance??

5. What is shift-invariancy??  Shift-variant??

6. Aliasing

7. Aliasing  Fourier Transform & Frequency Domain 𝑡𝑡 Ω 𝑋𝑋 Ω = � −∞ ∞ 𝑥𝑥 𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡 𝑑𝑑𝑑𝑑

8. Aliasing  Down-sampling in frequency domain 𝑇𝑇 𝑥𝑥 𝑡𝑡 = � −∞ ∞ 𝑥𝑥 𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡 𝑑𝑑𝑑𝑑 = 𝑋𝑋(Ω) 𝑇𝑇 𝑥𝑥 𝑎𝑎𝑡𝑡 = � −∞ ∞ 𝑥𝑥 𝑎𝑎𝑡𝑡 𝑒𝑒−𝑗𝑗Ω𝑡𝑡 𝑑𝑑𝑑𝑑 = 1 |𝑎𝑎| � −∞ ∞ 𝑥𝑥 𝑡𝑡′ 𝑒𝑒−𝑗𝑗 Ω 𝑎𝑎 𝑡𝑡′ 𝑑𝑑𝑑𝑑′ 𝑇𝑇 𝑥𝑥 𝑎𝑎𝑡𝑡 = 1 |𝑎𝑎| 𝑋𝑋(Ω/𝑎𝑎)

9. Aliasing  Down-sampling in frequency domain 𝑡𝑡 Ω 𝑡𝑡 Ω

10. Aliasing  Discrete Signal in Frequency Domain (Discrete Time Fourier Transform) 𝜔𝜔𝑛𝑛 2𝜋𝜋−2𝜋𝜋 𝑋𝑋 𝜔𝜔 = � 𝑛𝑛=−∞ ∞ 𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗 𝑋𝑋 𝜔𝜔 + 2𝜋𝜋 = � 𝑛𝑛=−∞ ∞ 𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗𝑗 = � 𝑛𝑛=−∞ ∞ 𝑥𝑥 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗 = 𝑋𝑋(𝜔𝜔)

11. Aliasing  Discrete Down-Sampling in Frequency Domain 𝑓𝑓 𝑓𝑓 𝑛𝑛 2𝜋𝜋−2𝜋𝜋 𝑛𝑛 2𝜋𝜋−2𝜋𝜋 Aliasing Aliasing

12. Aliasing  Aliasing (Example)

13. Aliasing in CNN  Max pooling  Average pooling  Strided convolution 0 0 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 Max-pooling Max-pooling

14. Anti-aliasing

15. Anti-Aliasing  Shift Invariancy & Shift Equivariance • Shift Equivariance • Shift Invariancy Shift-Equivariant  Shift-Invariant

16. Anti-Aliasing  Anti-aliasing 𝑓𝑓2𝜋𝜋−2𝜋𝜋 Low Pass Filtering (Blurring) 𝑓𝑓2𝜋𝜋−2𝜋𝜋 𝑓𝑓2𝜋𝜋−2𝜋𝜋 Sampling

17. Anti-Aliasing  Anti-aliasing for max pooling 0 0 1 1 0 0 1 1 0 1 0 10 1 1 1 0 1 1 1 Max Sampling 0 1 1 0 0 1 1 0 1 1 1 11 1 1 0 1 1 1 0 Shift-Equivariant!!

18. Anti-Aliasing  Anti-aliasing for max pooling 0 1 1 1 0 1 1 1 0.5 1 0.5 10.5 1 1 0.5 0.5 1 1 0.5 Blurring Subsampling 1 1 1 0 1 1 1 0 1 0.5 1 0.51 1 0.5 0.5 1 1 0.5 0.5 Max

19. Anti-Aliasing  Anti-aliasing for various sampling operations in CNN • MaxPool • AveragePool • StrideConv

20. Anti-Aliasing  Down-sampling Kernels

21. Results

22. Results  Improvement in Consistency

23. Results  Improvement in Accuracy

24. Results  Improvement in Accuracy

25. Results  Improvement in Image Translation

26. Thank You!