Stanford AI Lab’s ICCV 2021 Papers: Cutting-Edge Research Unveiled
Introduction:
Welcome to the International Conference on Computer Vision (ICCV 2021)! This highly anticipated event will be held virtually next week, showcasing cutting-edge research and advancements in the field of computer vision. Hosted by Stanford University, ICCV 2021 brings together experts, researchers, and industry professionals to share their work and insights.
With a diverse range of accepted papers, this conference covers a wide spectrum of topics, including medical image recognition, shape generation, object pose tracking, human-object relationships, self-supervised learning, and much more. Each paper offers unique perspectives and innovative approaches to address the challenges in computer vision.
Explore the list of accepted papers, which includes captivating visuals, informative videos, and insightful blogs. Take this opportunity to connect with the authors directly to gain deeper insights into their research and discover the latest advancements happening at Stanford.
Don’t miss out on this exciting gathering of visionaries and join us at ICCV 2021 to explore the forefront of computer vision research.
Full Article: Stanford AI Lab’s ICCV 2021 Papers: Cutting-Edge Research Unveiled
International Conference on Computer Vision (ICCV 2021) is set to be hosted virtually next week. The conference is a highly anticipated event that showcases the latest research and advancements in the field of computer vision. Stanford Artificial Intelligence Laboratory (SAIL) is thrilled to announce their participation and share the exciting work that will be presented at the conference.
List of Accepted Papers
The following are some of the accepted papers that will be presented at ICCV 2021:
1. GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition
– Author: Mars Huang
– Contact: mschuang@stanford.edu
– Keywords: medical image, self-supervised learning, multimodal fusion
2. 3D Shape Generation and Completion Through Point-Voxel Diffusion
– Authors: Linqi Zhou, Yilun Du, Jiajun Wu
– Contact: linqizhou@stanford.edu
– Links: [Paper](https://arxiv.org/pdf/2104.03670.pdf) | [Video](https://www.youtube.com/watch?v=64jl79i6HNY) | [Website](https://alexzhou907.github.io/pvd)
– Keywords: diffusion, shape generation
3. CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
– Authors: Yijia Weng*, He Wang*, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas J. Guibas
– Contact: yijiaw@stanford.edu
– Award nominations: Oral Presentation
– Links: [Paper](https://arxiv.org/abs/2104.03437) | [Video](https://www.youtube.com/watch?v=EkcCEj7gZGg) | [Website](https://yijiaweng.github.io/CAPTRA/)
– Keywords: category-level object pose tracking, articulated objects
4. Detecting Human-Object Relationships in Videos
– Authors: Jingwei Ji, Rishi Desai, Juan Carlos Niebles
– Contact: jingweij@cs.stanford.edu
– Links: Paper
– Keywords: human-object relationships, video, detection, transformer, spatio-temporal reasoning
5. Geography-Aware Self-Supervised Learning
– Authors: Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
– Contact: kayush@cs.stanford.edu, chenlin@stanford.edu
– Links: [Paper](https://arxiv.org/pdf/2011.09980.pdf) | [Website](https://geography-aware-ssl.github.io/)
– Keywords: self-supervised learning, contrastive learning, remote sensing, spatio-temporal, classification, object detection, segmentation
6. HuMoR: 3D Human Motion Model for Robust Pose Estimation
– Authors: Davis Rempe, Tolga Birdal, Aaron Hertzmann, Jimei Yang, Srinath Sridhar, Leonidas Guibas
– Contact: drempe@stanford.edu
– Award nominations: Oral Presentation
– Links: [Paper](https://geometry.stanford.edu/projects/humor/docs/humor.pdf) | [Website](https://geometry.stanford.edu/projects/humor/)
– Keywords: 3d human pose estimation; 3d human motion; generative modeling
7. Learning Privacy-preserving Optics for Human Pose Estimation
– Authors: Carlos Hinojosa, Juan Carlos Niebles, Henry Arguello
– Contact: carlos.hinojosa@saber.uis.edu.co
– Links: [Paper](https://carloshinojosa.me/files/ICCV2021/05401.pdf) | [Website](https://carloshinojosa.me/project/privacy-hpe/)
– Keywords: computational photography; fairness, accountability, transparency, and ethics in vision; gestures and body pose
8. Learning Temporal Dynamics from Cycles in Narrated Video
– Authors: Dave Epstein, Jiajun Wu, Cordelia Schmid, Chen Sun
– Contact: jiajunwu@cs.stanford.edu
– Links: [Paper](https://arxiv.org/pdf/2101.02337.pdf) | [Website](https://dave.ml/mmcc/)
– Keywords: multi-modal learning, cycle consistency, video
9. Vector Neurons: A General Framework for SO(3)-Equivariant Networks
– Authors: Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas Guibas
– Contact: congyue@stanford.edu
– Links: [Paper](https://arxiv.org/pdf/2104.12229.pdf) | [Video](https://www.youtube.com/watch?v=aJy4eMvdTpA&t=4s) | [Website](https://cs.stanford.edu/~congyue/vnn/)
– Keywords: pointcloud network, rotation equivariance, rotation invariance
Summary: Stanford AI Lab’s ICCV 2021 Papers: Cutting-Edge Research Unveiled
The International Conference on Computer Vision (ICCV 2021) will be held virtually next week. Stanford University’s research group, SAIL, will be presenting their work at the conference. This article provides a list of accepted papers along with links to related resources such as papers, videos, and blogs. The topics covered include medical image recognition, 3D shape generation, object pose tracking, human-object relationships in videos, self-supervised learning, 3D human pose estimation, and more. If you are interested in any of these topics or want to learn more about the work happening at Stanford, don’t hesitate to contact the authors directly.
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