cvpaper.challenge の Meta Study Group 発表スライド
cvpaper.challenge はコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有します。2019の目標「トップ会議30+本投稿」「2回以上のトップ会議網羅的サーベイ」
http://xpaperchallenge.org/cv/
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.
cvpaper.challenge の Meta Study Group 発表スライド
cvpaper.challenge はコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有します。2019の目標「トップ会議30+本投稿」「2回以上のトップ会議網羅的サーベイ」
http://xpaperchallenge.org/cv/
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
This document summarizes recent advances in single image super-resolution (SISR) using deep learning methods. It discusses early SISR networks like SRCNN, VDSR and ESPCN. SRResNet is presented as a baseline method, incorporating residual blocks and pixel shuffle upsampling. SRGAN and EDSR are also introduced, with EDSR achieving state-of-the-art PSNR results. The relationship between reconstruction loss, perceptual quality and distortion is examined. While PSNR improves yearly, a perception-distortion tradeoff remains. Developments are ongoing to produce outputs that are both accurately restored and naturally perceived.
Semi supervised, weakly-supervised, unsupervised, and active learningYusuke Uchida
An overview of semi supervised learning, weakly-supervised learning, unsupervised learning, and active learning.
Focused on recent deep learning-based image recognition approaches.
研究室の輪講で使った古いスライド。物体検出の黎明期からシングルショット系までのまとめ。
Old slides used in a lab lecture. A summary of object detection from its early days to single-shot systems.
フォント不足による表示崩れがあります(筑紫A丸ゴシック、Montserratを使用)。
[DL輪読会]Neural Radiance Flow for 4D View Synthesis and Video Processing (NeRF...Deep Learning JP
Neural Radiance Flow (NeRFlow) is a method that extends Neural Radiance Fields (NeRF) to model dynamic scenes from video data. NeRFlow simultaneously learns two fields - a radiance field to reconstruct images like NeRF, and a flow field to model how points in space move over time using optical flow. This allows it to generate novel views from a new time point. The model is trained end-to-end by minimizing losses for color reconstruction from volume rendering and optical flow reconstruction. However, the method requires training separate models for each scene and does not generalize to unknown scenes.
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
This document summarizes recent advances in single image super-resolution (SISR) using deep learning methods. It discusses early SISR networks like SRCNN, VDSR and ESPCN. SRResNet is presented as a baseline method, incorporating residual blocks and pixel shuffle upsampling. SRGAN and EDSR are also introduced, with EDSR achieving state-of-the-art PSNR results. The relationship between reconstruction loss, perceptual quality and distortion is examined. While PSNR improves yearly, a perception-distortion tradeoff remains. Developments are ongoing to produce outputs that are both accurately restored and naturally perceived.
Semi supervised, weakly-supervised, unsupervised, and active learningYusuke Uchida
An overview of semi supervised learning, weakly-supervised learning, unsupervised learning, and active learning.
Focused on recent deep learning-based image recognition approaches.
研究室の輪講で使った古いスライド。物体検出の黎明期からシングルショット系までのまとめ。
Old slides used in a lab lecture. A summary of object detection from its early days to single-shot systems.
フォント不足による表示崩れがあります(筑紫A丸ゴシック、Montserratを使用)。
[DL輪読会]Neural Radiance Flow for 4D View Synthesis and Video Processing (NeRF...Deep Learning JP
Neural Radiance Flow (NeRFlow) is a method that extends Neural Radiance Fields (NeRF) to model dynamic scenes from video data. NeRFlow simultaneously learns two fields - a radiance field to reconstruct images like NeRF, and a flow field to model how points in space move over time using optical flow. This allows it to generate novel views from a new time point. The model is trained end-to-end by minimizing losses for color reconstruction from volume rendering and optical flow reconstruction. However, the method requires training separate models for each scene and does not generalize to unknown scenes.
文献紹介:SlowFast Networks for Video RecognitionToru Tamaki
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, Kaiming He, SlowFast Networks for Video Recognition, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6202-6211
https://openaccess.thecvf.com/content_ICCV_2019/html/Feichtenhofer_SlowFast_Networks_for_Video_Recognition_ICCV_2019_paper.html
The document contains a date, 1615, repeated twice. There is no other text or context provided, so a summary is not possible based on the extremely limited information given.
7. Side Window Filter (SWF)[1-1]
• 通常のフィルタ:注目画素を中心とした近傍で計算
• side window:注目画素を端においた近傍で計算
– 斜め等様々なパターン(下例)が考えられるが、計算効率上8つに絞っている
7
注目画素
L R U D NW NE SW SE
…
7x7のGaussian Filterを例に
13. 参考文献
• [1-1] Yin, Hui & Gong, Yuanhao & Qiu, Guoping. (2019). Side Window Filtering.
8750-8758. 10.1109/CVPR.2019.00896.
• [1-2] Side Window Filtering (CVPR2019 oral, #5176)
https://github.com/YuanhaoGong/SideWindowFilter
• [1-3] F. Durand and J. Dorsey. Fast bilateral filtering for the display of high-
dynamic-range images. ACM Trans. on Graphics, 21(3):257–266, 2002.
• [1-4] A. Levin, D. Lischinski, and Y. Weiss. Colorization using optimization. ACM
Trans on Graphics, 23(3):689–694, 2004.
13
26. 参考文献
• [2-1] Gong, Yuanhao & Sbalzarini, Ivo. (2017). Curvature Filters Efficiently Reduce
Certain Variational Energies. IEEE Transactions on Image Processing. 26. 1786-
1798. 10.1109/TIP.2017.2658954.
• [2-2] Curvature filters are efficient solvers for variational models.
https://github.com/YuanhaoGong/CurvatureFilter
• [2-3] https://slidesplayer.net/slide/16186926/
• [2-4] M. Ibrahim, K. Chen, and C. Brito-Loeza. (2015). “A novel variational model
for image registration using Gaussian curvature.” [Online]. Available:
https://arxiv.org/abs/1504.07643
• [2-5] S.-H. Lee and J. K. Seo, “Noise removal with Gauss curvature-driven
diffusion,” IEEE Trans. Image Process., vol. 14, no. 7, pp. 904–909, Jul. 2005
• [2-6] T. Goldstein and S. Osher, “The split Bregman method for L1-regularized
problems,” SIAM J. Imag. Sci., vol. 2, no. 2, pp. 323–343, 2009.
• [2-7] A. Chambolle and T. Pock, “A first-order primal-dual algorithm for convex
problems with applications to imaging,” J. Math. Imag. Vis., vol. 40, no. 1, pp.
120–145, 2011.
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