2019 cvpr paper_overview
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The document provides an overview of the CVPR 2019 conference, highlighting the increasing number of submissions and key statistics compared to previous years. It summarizes various notable papers presented at the conference, primarily focusing on advancements in computer vision topics such as motion synthesis, semantic image synthesis, and object detection among others. Additionally, it mentions the significance of certain keywords and trends observed in the submissions.
2019 cvpr paper_overview
- 1. Intelligence Machine Vision Lab Strictly Confidential 2019 CVPR paper overview SUALAB Ho Seong Lee
- 2. 2Type A-3 Contents • CVPR 2019 Statistics • 20 paper-one page summary
- 3. 3Type A-3 CVPR 2019 Statistics • What is CVPR? • Conference on Computer Vision and Pattern Recognition (CVPR) • CVPR was first held in 1983 and has been held annually • CVPR 2019: June 16th – June 20th in Long Beach, CA
- 4. 4Type A-3 CVPR 2019 Statistics • CVPR 2019 statistics • The total number of papers is increasing every year and this year has increased significantly! • We can visualize main topic using title of paper and simple python script! • https://github.com/hoya012/CVPR-Paper-Statistics 28.4% 30% 29.9% 29.6% 25.1%
- 5. 5Type A-3 CVPR 2019 Statistics 2019 CVPR paper statistics
- 6. 6Type A-3 CVPR 2018 Statistics 2018 CVPR paper statistics Compared to 2018 Statistics..
- 7. 7Type A-3 CVPR 2018 vs CVPR 2019 Statistics • Most of the top keywords were maintained • Image, detection, 3d, object, video, segmentation, adversarial, recognition, visual … • “graph”, “cloud”, “representation” are about twice as frequent • graph : 15 → 45 • representation: 25 → 48 • cloud: 16 → 35
- 8. 8Type A-3 Before beginning.. • It does not mean that it is not an interesting article because it is not in the list. • Since I mainly studied Computer Vision, most papers that I will discuss today are Computer Vision papers.. • Topics not covered today • Natural Language Processing • Reinforcement Learning • Robotics • Etc..?
- 9. 9Type A-3 1. Learning to Synthesize Motion Blur (oral) • Synthesizing a motion blurred image from a pair of unblurred sequential images • Motion blur is important in cinematography, and artful photo • Generate a large-scale synthetic training dataset of motion blurred images Recommended reference: “Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation”, 2018 CVPR
- 10. 10Type A-3 2. Semantic Image Synthesis with Spatially-Adaptive Normalization (oral) • Synthesizing photorealistic images given an input semantic layout • Spatially-adaptive normalization can keep semantic information • This model allows user control over both semantic and style as synthesizing images Demo Code: https://github.com/NVlabs/SPADE
- 11. 11Type A-3 3. SiCloPe: Silhouette-Based Clothed People (Oral) • Reconstruct a complete and textured 3D model of a person from a single image • Use 2D Silhouettes and 3D joints of a body pose to reconstruct 3D mesh • An effective two-stage 3D shape reconstruction pipeline • Predicting multi-view 2D silhouettes from single input segmentation • Deep visual hull based mesh reconstruction technique Recommended reference: “BodyNet: Volumetric Inference of 3D Human Body Shapes”, 2018 ECCV
- 12. 12Type A-3 4. Im2Pencil: Controllable Pencil Illustration from Photographs • Propose controllable photo-to-pencil translation method • Modeling pencil outline(rough, clean), pencil shading(4 types) • Create training data pairs from online websites(e.g., Pinterest) and use image filtering techniques Demo Code: https://github.com/Yijunmaverick/Im2Pencil
- 13. 13Type A-3 5. End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image • End-to-end solution to synthesize a time-lapse video from single image • Use time-lapse videos and image sequences during training • Use only single image during inference Input image(single)
- 14. 14Type A-3 6. StoryGAN: A Sequential Conditional GAN for Story Visualization • Propose a new task called Story Visualization using GAN • Sequential conditional GAN based StoryGAN • Story Encoder – stochastic mapping from story to an low-dimensional embedding vector • Context Encoder – capture contextual information during sequential image generation • Two Discriminator – Image Discriminator & Story Discriminator Context Encoder
- 15. 15Type A-3 7. Image Super-Resolution by Neural Texture Transfer (oral) • Improve “RefSR” even when irrelevant reference images are provided • Traditional Single Image Super-Resolution is extremely challenging (ill-posed problem) • Reference-based(RefSR) utilizes rich texture from HR references .. but.. only similar Ref images • Adaptively transferring the texture from Ref Images according to their texture similarity Recommended reference: “CrossNet: An end-to-end reference-based super resolution network using cross-scale warping”, 2018 ECCV Similar Different
- 16. 16Type A-3 8. DVC: An End-to-end Deep Video Compression Framework (oral) • Propose the first end-to-end video compression deep model • Conventional video compression use predictive coding architecture and encode corresponding motion information and residual information • Taking advantage of both classical compression and neural network • Use learning based optical flow estimation
- 17. 17Type A-3 9. Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search (oral) • Defense adversarial attack using Big-data and image manifold • Assume that adversarial attack move the image away from the image manifold • A successful defense mechanism should aim to project the images back on the image manifold • For tens of billions of images, search a nearest-neighbor images (K=50) and use them • Also propose two novel attack methods to break nearest neighbor defenses
- 18. 18Type A-3 10. Bag of Tricks for Image Classification with Convolutional Neural Networks • Examine a collection of some refinements and empirically evaluate their impact • Improve ResNet-50’s accuracy from 75.3% to 79.29% on ImageNet with some refinements • Efficient Training • FP32 with BS=256 → FP16 with BS=1024 with some techniques • Training Refinements: • Cosine Learning Rate Decay / Label Smoothing / Knowledge Distillation / Mixup Training • Transfer from classification to Object Detection, Semantic Segmentation Linear scaling LR LR warmup Zero γ initialization in BN No bias decay Result of Efficient Training Result of Training refinements ResNet tweaks
- 19. 19Type A-3 11. Fully Learnable Group Convolution for Acceleration of Deep Neural Networks • Automatically learn the group structure in training stage with end-to-end manner • Outperform standard group convolution • Propose an efficient strategy for index re-ordering
- 20. 20Type A-3 12. ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch (oral) • Explore to train object detectors from scratch robustly • Almost SOTA detectors are fine-tuned from pretrained CNN (e.g., ImageNet) • The classification and detection have different degrees of sensitivity to translation • The architecture is limited by the classification network(backbone) → inconvenience! • Find that one of the overlooked points is BatchNorm! Recommended reference: “DSOD: Learning Deeply Supervised Object Detectors from Scratch”, 2017 ICCV
- 21. 21Type A-3 13. Precise Detection in Densely Packed Scenes • Propose precise detection in densely packed scenes • In real-world, there are many applications of object detection (ex, detection and count # of object) • In densely packed scenes, SOTA detector can’t detect accurately (1) layer for estimating the Jaccard index (2) a novel EM merging unit (3) release SKU-110K dataset
- 22. 22Type A-3 14. SIXray: A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images • Present a large-scale dataset and establish a baseline for security inspection X-ray • Total 1,059,231 X-ray images in which 6 classes of 8,929 prohibited items • Propose an approach named class-balanced hierarchical refinement(CHR) and class-balanced loss function
- 23. 23Type A-3 15. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressio • Address the weaknesses of IoU and introduce generalized version(GIoU) • Intersection over Union(IoU) is the most popular evaluation metric used in object detection • But, there is a gap between optimizing distance losses and maximizing IoU • Introducing generalized IoU as both a new loss and a new metric
- 24. 24Type A-3 16. Bounding Box Regression with Uncertainty for Accurate Object Detection • Propose novel bounding box regression loss with uncertainty • Most of datasets have ambiguities and labeling noise of bounding box coordinate • Network can learns to predict localization variance for each coordinate
- 25. 25Type A-3 17. UPSNet: A Unified Panoptic Segmentation Network (oral) • Propose a unified panoptic segmentation network(UPSNet) • Semantic segmentation + Instance segmentation = panoptic segmentation • Semantic Head + Instance Head + Panoptic head → end-to-end manner Recommended reference: “Panoptic Segmentation”, 2018 arXiv Countable objects → things Uncountable objects → stuff Deformable Conv Mask R-CNN Parameter-free
- 26. 26Type A-3 18. SFNet: Learning Object-aware Semantic Correspondence (Oral) • Propose SFNet for semantic correspondence problem • Propose to use images annotated with binary foreground masks and synthetic geometric deformations during training • Manually selecting point correspondences is so expensive!! • Outperform SOTA on standard benchmarks by a significant margin
- 27. 27Type A-3 19. Fast Interactive Object Annotation with Curve-GC • Propose end-to-end fast interactive object annotation tool (Curve-GCN) • Predict all vertices simultaneously using a Graph Convolutional Network, (→ Polygon-RNN X) • Human annotator can correct any wrong point and only the neighboring points are affected Recommended reference: “Efficient interactive annotation of segmentation datasets with polygon-rnn++ ”, 2018 CVPR Code: https://github.com/fidler-lab/curve-gcn Correction!
- 28. 28Type A-3 20. FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference • Propose image-level WSSS method using stochastic inference (dropout) • Localization maps(CAM) only focus on the small parts of objects → Problem • FickleNet allows a single network to generate multiple CAM from a single image • Does not require any additional training steps and only adds a simple layer Full Stochastic Both Training and Inference
- 29. 29Type A-3 Related Post.. • In my personal blog, there are similar works • SIGGRAPH 2018 • NeurIPS 2018 • ICLR 2019 https://hoya012.github.io/
- 30. Thank you