Paper registration is this week! Here are Adobes contributions to CVPR 2022. I have a couple of papers that are among the top-10 most cited papers published in top-tier conferences for each year. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. CVF Computer Vision and Pattern Recognition Conference (CVPR 2022). FAQ: https: . Does Robustness on ImageNet Transfer to Downstream Tasks? Copyright 2022 Adobe. (Estimation) 5. Here are the research categories at CVPR 2022 sorted by number of papers in each focus: From this list we can see the top two categories that researchers focus on: detection/recognition and generation. New Orleans, Louisiana, June 19, 20, 2022 Workshops 10/19 - CVPR 2022 invites (self-)nominations for reviewers using this form 9/30 - All authors should carefully review the Author Guidelines and Ethics Guidelines, which contain a number of important updates for 2022 09/25 - CVPR 2022 Tutorials Call For Proposals updated here 06/22 - CVPR 2022 paper submission deadline will be Nov 16th, 2021. We use cookies on this site to enhance your user experience. By clicking the Accept button, you agree to us doing so. CVPR-2022-Oral-Paper-list. When one says computer vision, a number of things come to mind such as self-driving cars and facial recognition. It is common in machine learning today to pre-train a model on a general domain (like the entire web) with a general task, and then fine-tune that model into the domain that it is being applied to (like identifying missing screws in a factory). The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. June 21 -23 Expo, CVPR 2022 will be a hybrid conference, with both in-person and virtual attendance options. Jun 22, 2022: MAXIM selected as 1 of the best paper nomination! Colab demo by @deshwalmahesh; Replicate web demo . These CVPR 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. - Transformers are found to generalize better than traditional CNNs as they are applied to tasks beyond ImageNet, a popular computer vision classification benchmark. @InProceedings{Chan_2022_CVPR, author = {Chan, Eric R. and Lin, Connor Z. and Chan, Matthew A. and Nagano, Koki and Pan, Boxiao and De Mello, Shalini and Gallo, Orazio and Guibas, Leonidas J. and Tremblay, Jonathan and Khamis, Sameh and Karras . (Segmentation) 3. All rights reserved. This platform is a part of the AI4Europe project that has received funding from the European Unions Horizon Europe research and innovation programme under Grant Agreement n 101070000. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Few-Shot Object Detection With Fully Cross-Transformer. Apr 25, 2022: Added demos. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. More info. It can be hard to nail down the right "proposal" to feed a network to accurately describe what you are after. Mar 3, 2022: paper accepted to CVPR 2022! Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Robust Fine-Tuning of Zero-Shot Models - this paper finds that it is effective to keep a set of pre-trained weights along with fine-tuned weights when adapting across domains. This paper investigates how to generate proper proposals. These CVPR 2022 papers are the Open Access versions, provided by the Computer Vision Foundation. . Are Multimodal Transformers Robust to Missing Modality? The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. . Multi-modal Research Expanding What is Possible, Transfer Learning is Being Battle Hardened. A tag already exists with the provided branch name. For computer vision researchers at CVPR, computer vision means many things according to their focus. This material is presented to ensure timely dissemination of scholarly and technical work. Hopefully this shortened list was a helpful way to find important takeaways from this year's group of papers. This work has been done in collaboration with researchers from Singapore, US and Australia. . (Face) 7. From our view, the most important themes at CVPR 2022 this year boiled down to: The transformer architecture was originally introduced in the NLP world for machine translation. Does Robustness on ImageNet Transfer to Downstream Tasks? Detection involves making inference from an image like object detection and generation involves generating new images, like DALL E. Other categories at CVPR are more foundational, such as deep learning architectures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. Detection involves making inference from an image like object detection and generation involves generating new images, like DALL E. oral code CVPR 2022 Oral; Video-QA Measuring Compositional Consistency for Video Question Answering; 29.Augmented Reality/Virtual Reality/Robotics. Accepted Papers. Deadline: Fri, Nov 4, 2022 11:59pm Pacific Time. Nearly all these papers are the result of research internships or other collaborations with university students and faculty. Jan 9, 2022: initial uploads to Arxiv; Quick Demos . (Remote Sensing Image) 12. This CVPR, authors cannot be added or deleted after the paper registration deadline, and authors cannot be reordered after the paper submission deadline. At the 2022 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR) this week, Adobe has co-authored a total of 48 papers, including 13 oral papers and 35 poster papers, plus 6 workshop papers. Categories at CVPR 2022 ranked by number of papers accepted From this list we can see the top two categories that researchers focus on: detection/recognition and generation. (CVPR), 2022 (Oral). Best Paper Nominee arXiv code : An Empirical Study . Few-shot detection is often used to measure how quickly new models adapt to new domains. Conference content hosted on the virtual platform will be available exclusively to CVPR registered attendees. For those interested, please check out Adobe Researchs careers page to learn more about internships and full-time career opportunities. Machine Learning @ Roboflow - building tools and artifacts like this one to help practitioners solve computer vision. The python script of downloading CVPR 2022 oral papers. (3D Vision) 8. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator based on the reward. For those of us in applied computer vision, tasks like object detection and instance segmentation come to mind. / (Text Detection/Recognition) 11. This material is presented to ensure timely dissemination of scholarly and technical work. Grounded Language-Image Pre-Training - GLIP learns across language and images - GLIP demonstrates state of the art performance on object detection COCO when fine-tuned and while less accurate, astonishing zero-shot performance. Learning To Prompt for Open-Vocabulary Object Detection With Vision-Language Mode - zero-shot description+image detection approaches require a prompt or "proposal". There are many papers released during each CVPR annual conference and you can access previous papers to see how the industry focus has evolved. 2. The python script of downloading CVPR 2022 oral papers. Recently, it has been found that rich deep learning representations are formed in multi-modal models, pushing the limits of what is possible - like generating an image from text, or providing a list of captions to draw detection predictions out of an image. The python script of downloading CVPR 2022 oral papers. The conference proceedings will be publicly available via the CVF website, with the final version posted to IEEE Xplore after the conference. Globetrotter: Connecting Languages by Connecting Images - Images are found to provide connecting semantics across human languages. In this post, we take the opportunity to reflect on the computer vision research landscape at CVPR 2022 and highlight our favorite research papers and themes. Are Multimodal Transformers Robust to Missing Modality? It has become increasingly evident that transformers do a better job of modeling most tasks, and the computer vision community is leaning into their adoption and implementation. New Orleans Ernest N. Morial Convention Center - This paper investigates whether multi-modal models learn representations that are general to semantics in general, not just the data types that they have seen. You signed in with another tab or window. Target Navigation Online Learning of Reusable Abstract Models for Object Goal Navigation; try-on Dressing in the Wild by Watching Dance Videos project Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space, Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts, Globetrotter: Connecting Languages by Connecting Images, Learning To Prompt for Open-Vocabulary Object Detection With Vision-Language Mode. &/ (Image&Video Retrieval/Video Understanding) 6. For Samsung's Toronto AI Center, this is the second time in two years they have earned such a chance, as they were also selected for oral presentation in 2020. A paper on learning from a limited data for human body/pose estimation from TAILOR researcher Hossein Rahmani, Lancaster University, has been accepted in the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2022) for oral presentation (acceptance rate is ~4%). (Medical Imaging) 10. In recent years, I have been interested in unsupervised learning and my explorative works are the top cited papers published in CVPR 2020, 2021, 2022. Use Roboflow to manage datasets, train models in one-click, and deploy to web, mobile, or the edge. A lot of work at CVPR was done on battle hardening these techniques. : cvproral session The Computer Vision and Pattern Recognition (CVPR) conference was held this week (June 2022) in New Orleans, pushing the boundaries of computer vision research. June 21 - 24 Main Conference Few-Shot Object Detection With Fully Cross-Transformer - when you do not have much data few-shot detection allows you to train a model quickly with just a few examples to learn from. CVPR2022 Papers (Papers/Codes/Demos) 1. (Image Processing) 4. Adobe authors have also contributed to the conference in many other ways, including co-organizing several workshops, area chairing, and reviewing papers. BokehMe: When Neural Rendering Meets Classical RenderingJuewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, Jianming Zhang, Ensembling Off-the-shelf Models for GAN TrainingNupur Kumari, Richard Zhang, Eli Shechtman, Jun-Yan Zhu, FaceFormer: Speech-Driven 3D Facial Animation with TransformersYingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura, GAN-Supervised Dense Visual AlignmentWilliam Peebles Jun-Yan Zhu, Richard Zhang, Antonio Torralba, Alexei Efros, Eli ShechtmanBest Paper Finalist, IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric ImagesKai Zhang, Fujun Luan, Zhengqi Li, Noah Snavely, MAT: Mask-Aware Transformer for Large Hole Image InpaintingWenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, Jiaya Jia, NeRFusion: Fusing Radiance Fields for Large-Scale Scene ReconstructionXiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli, Hao Su, Zexiang Xu, Point-NeRF: Point-based Neural Radiance FieldsQiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, Ulrich Neumann, StyleSDF: High-Resolution 3D-Consistent Image and Geometry GenerationRoy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, Ira Kemelmacher-Shlizerman, The Implicit Values of a Good Hand Shake: Handheld Multi-Frame Neural Depth RefinementIlya Chugunov, Yuxuan Zhang, Zhihao Xia, Xuaner (Cecilia) Zhang, Jiawen Chen, Felix Heide, Towards Layer-wise Image VectorizationXu Ma, Yuqian Zhou, Xingqian Xu, Bin Sun, Valerii Filev, Nikita Orlov, Yun Fu, Humphrey Shi, vCLIMB: A Novel Video Class Incremental Learning BenchmarkAndrs Villa, Kumail Alhamoud, Juan Len Alczar, Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance SegmentationSu Ho Han, Sukjun Hwang, Seoung Wug Oh, Yeonchool Park, Hyunwoo Kim, Min-Jung Kim, Seon Joo Kim, APES: Articulated Part Extraction from Sprite SheetsZhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi, Evangelos Kalogerakis, Audio-driven Neural Gesture Reenactment with Video Motion GraphsYang Zhou; Jimei Yang; Dingzeyu Li; Jun Saito; Deepali Aneja; Evangelos Kalogerakis, Boosting Robustness of Image Matting with Context Assembling and Strong Data AugmentationYutong Dai, Brian Price, He Zhang, Chunhua Shen, Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints to Better Classify Objects in VideosSukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim, Controllable Animation of Fluid Elements in Still ImagesAniruddha Mahapatra, Kuldeep Kulkarni, Cross Modal Retrieval with Querybank NormalisationSimion-Vlad Bogolin, Ioana Croitoru, Hailin Jin, Yang Liu, Samuel Albanie, EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal RetrievalHaoyu Ma, Handong Zhao, Zhe Lin, Ajinkya Kale, Zhangyang Wang, Tong Yu, Jiuxiang Gu, Sunav Choudhary, Xiaohui Xie, Estimating Example Difficulty using Variance of GradientsChirag Agarwal, Daniel Dsouza, Sara Hooker, Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep ModelsZhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren, Focal length and object pose estimation via render and compareGeorgy Ponimatkin, Yann Labb, Bryan Russell, Mathieu Aubry, Josef Sivic, Generalizing Interactive Backpropagating Refinement for Dense Prediction NetworksFanqing Lin, Brian Price, Tony Martinez, GIRAFFE HD: A High-Resolution 3D-aware Generative ModelYang Xue, Yuheng Li, Krishna Kumar Singh, Yong Jae, GLASS: Geometric Latent Augmentation for Shape SpacesSanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra, High Quality Segmentation for Ultra High-resolution ImagesTiancheng Shen, Yuechen Zhang, Lu Qi, Jason Kuen, Xingyu Xie, Jianlong Wu, Zhe Lin, Jiaya Jia, InsetGAN for Full-Body Image GenerationAnna Frhstck, Krishna Kumar Singh, Eli Shechtman, Niloy Mitra, Peter Wonka, Jingwan Lu, Its Time for Artistic Correspondence in Music and VideoDdac Surs, Carl Vondrick, Bryan Russell, Justin Salamon, Layered Depth Refinement with Mask GuidanceSoo Ye Kim, Jianming Zhang, Simon Niklaus, Yifei Fan, Simon Chen, Zhe Lin, Munchurl Kim, Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single CameraJae Shin Yoon, Duygu Ceylan, Tuanfeng Wang, Jingwan Lu, Jimei Yang, Zhixin Shu, Hyun Soo Park, Lite Vision Transformer with Enhanced Self-AttentionChenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe Lin, Alan Yuille, MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio DescriptionsMattia Soldan, Alejandro Pardo, Juan Len Alczar, Fabian Caba Heilbron, Chen Zhao, Silvio Giancola, Bernard Ghanem, Many-to-many Splatting for Efficient Video Frame InterpolationPing Hu, Simon Niklaus, Stan Sclaroff, Kate Saenko, Neural Convolutional SurfacesLuca Morreale, Noam Aigerman, Paul Guerrero, Vladimir Kim, Niloy Mitra, Neural Volumetric Object SelectionZhongzheng Ren, Aseem Agarwala, Bryan Russell, Alexander Schwing, Oliver Wang, Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape PriorsYun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg, On Aliased Resizing and Surprising Subtleties in GAN EvaluationGaurav Parmar, Richard Zhang, Jun-Yan Zhu, Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-LabelingDat Huynh, Jason Kuen, Zhe Lin, Jiuxiang Gu, Ehsan Elhamifar, Per-Clip Video Object SegmentationKwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon, Joon-Young Lee, PhotoScene: Physically-Based Material and Lighting Transfer for Indoor ScenesYu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu, Zexiang Xu, Milo Haan, Kalyan Sunkavalli, Manmohan Chandraker, RigNeRF: Fully Controllable Neural 3D PortraitsShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu, ShapeFormer: Transformer-based Shape Completion via Sparse RepresentationXingguang Yan, Liqiang Lin, Niloy Mitra, Dani Lischinski, Danny Cohen-Or, Hui Huang, SketchEdit: Mask-Free Local Image Manipulation with Partial SketchesYu Zeng, Zhe Lin, Vishal M. Patel, Spatially-Adaptive Multilayer Selection for GAN Inversion and EditingGaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, Krishna Kumar Singh, Towards Language-Free Training for Text-to-Image GenerationYufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun, Unsupervised Learning of De-biased Representation with Pseudo-bias AttributeSeonguk Seo, Joon-Young Lee, Bohyung Han, ARIA: Adversarially Robust Image Attribution for Content ProvenanceMaksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John CollomossePresented at Workshop on Media Forensics, Integrating Pose and Mask Predictions for Multi-person in VideosMiran Heo, Sukjun Hwang, Seoung Wug Oh, Joon-Young Lee, Seon Joo KimPresented at Efficient Deep Learning for Computer Vision Workshop, MonoTrack: Shuttle trajectory reconstruction from monocular badminton videoPaul Liu, Jui-Hsien WangPresented at Workshop on Computer Vision in Sports, The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body EstimationZhe Wang, Jimei Yang, Charless FowlkesPresented at Workshop and Competition on Affective Behavior Analysis in-the-wild, User-Guided Variable Rate Learned Image CompressionRushil Gupta, Suryateja BV, Nikhil Kapoor, Rajat Jaiswal, Sharmila Reddy Nangi, Kuldeep KulkarniPresented atChallenge and Workshop on Learned Image Compression, Video-ReTime: Learning Temporally Varying Speediness for Time RemappingSimon Jenni, Markus Woodson, Fabian Caba HeilbronPresented at Workshop: AI for Content Creation, AI for Content Creation WorkshopCynthia Lu, Sketch-oriented Deep LearningJohn Collomosse, AI for Content Creation WorkshopRichard Zhang, Dugyu Ceylan, Holistic Video Understanding workshopVishy Swaminathan, LatinX in AI WorkshopLuis Figueroa, Matheus Gadelha, New Trends in Image Restoration and Enhancement WorkshopRichard Zhang, BokehMe: When Neural Rendering Meets Classical Rendering, Ensembling Off-the-shelf Models for GAN Training, FaceFormer: Speech-Driven 3D Facial Animation with Transformers, IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images, MAT: Mask-Aware Transformer for Large Hole Image Inpainting, NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction, Point-NeRF: Point-based Neural Radiance Fields, StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation, The Implicit Values of a Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement, vCLIMB: A Novel Video Class Incremental Learning Benchmark, VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation, APES: Articulated Part Extraction from Sprite Sheets, Audio-driven Neural Gesture Reenactment with Video Motion Graphs, Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation, Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints to Better Classify Objects in Videos, Controllable Animation of Fluid Elements in Still Images, Cross Modal Retrieval with Querybank Normalisation, EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval, Estimating Example Difficulty using Variance of Gradients, Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models, Focal length and object pose estimation via render and compare, Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks, GIRAFFE HD: A High-Resolution 3D-aware Generative Model, GLASS: Geometric Latent Augmentation for Shape Spaces, High Quality Segmentation for Ultra High-resolution Images, Its Time for Artistic Correspondence in Music and Video, Layered Depth Refinement with Mask Guidance, Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera, Lite Vision Transformer with Enhanced Self-Attention, MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions, Many-to-many Splatting for Efficient Video Frame Interpolation, Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors, On Aliased Resizing and Surprising Subtleties in GAN Evaluation, Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling, PhotoScene: Physically-Based Material and Lighting Transfer for Indoor Scenes, RigNeRF: Fully Controllable Neural 3D Portraits, ShapeFormer: Transformer-based Shape Completion via Sparse Representation, SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches, Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing, Towards Language-Free Training for Text-to-Image Generation, Unsupervised Learning of De-biased Representation with Pseudo-bias Attribute, ARIA: Adversarially Robust Image Attribution for Content Provenance, Integrating Pose and Mask Predictions for Multi-person in Videos, Efficient Deep Learning for Computer Vision Workshop, MonoTrack: Shuttle trajectory reconstruction from monocular badminton video, The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation, Workshop and Competition on Affective Behavior Analysis in-the-wild, User-Guided Variable Rate Learned Image Compression, Challenge and Workshop on Learned Image Compression, Video-ReTime: Learning Temporally Varying Speediness for Time Remapping, New Trends in Image Restoration and Enhancement Workshop.
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