Workshops
9th New Trends in Image Restoration and Enhancement Workshop and Challenges
Radu Timofte ⋅ Zongwei Wu
HYBRID: Room **Arch 204**, Seattle Convention Center
SCHEDULE: https://www.cvlai.net/ntire/2024/#schedule
Mon Jun 08:00 - 18:00 PDT
Lunch Break: 12:00-13:00
Poster session: 16:00-18:00
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SCHEDULE: https://www.cvlai.net/ntire/2024/#schedule
Mon Jun 08:00 - 18:00 PDT
Lunch Break: 12:00-13:00
Poster session: 16:00-18:00
Domain adaptation, Explainability and Fairness in AI for Medical Image Analysis (DEF-AI-MIA)
Stefanos Kollias
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SyntaGen: Harnessing Generative Models for Synthetic Visual Datasets
Khoi Nguyen
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The 3rd International Workshop on Federated Learning for Computer Vision (FedVision-2024)
Chen Chen
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4th Workshop on Physics Based Vision meets Deep Learning (PBDL2024)
Shaodi You
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1st Workshop on Urban Scene Modeling: Where Vision Meets Photogrammetry and Graphics
Jack Langerman ⋅ Ruisheng Wang
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4th International Workshop on Long-form Video Understanding: Towards Multimodal AI Assistant and Copilot
Mike Zheng Shou
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5th International Workshop on Large Scale Holistic Video Understanding
Mohsen Fayyaz
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The Fifth Workshop on Fair, Data-efficient, and Trusted Computer Vision
Srikrishna Karanam
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The 4th Workshop of Adversarial Machine Learning on Computer Vision: Robustness of Foundation Models
Aishan Liu
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First Workshop on Efficient and On-Device Generation (EDGE)
Felix Juefei Xu ⋅ Tingbo Hou
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4th Workshop on CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling
Urs Waldmann
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The 7th Workshop and Challenge Bridging the Gap between Computational Photography and Visual Recognition (UG2+)
Nicholas M Chimitt
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2nd Workshop on Scene Graphs and Graph Representation Learning
Azade Farshad
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1st Workshop on Dataset Distillation for Computer Vision
Saeed Vahidian
In the past decade, deep learning has been mainly advanced by training increasingly large models on increasingly large datasets which comes with the price of massive computation and expensive devices for their training.
As a result, research on designing state-of-the-art models gradually gets monopolized by large companies, while research groups with limited resources such as universities and small companies are unable to compete.
Reducing the training dataset size while preserving model training effects is significant for reducing the training cost, enabling green AI, and encouraging the university research groups to engage in the latest research.
This workshop focuses on the emerging research field of dataset distillation which aims to compress a large training dataset into a tiny informative one (e.g. 1\% of the size of the original data) while maintaining the performance of models trained on this dataset. Besides general-purpose efficient model training, dataset distillation can also greatly facilitate downstream tasks such as neural architecture/hyperparameter search by speeding up model evaluation, continual learning by producing compact memory, federated learning by reducing data transmission, and privacy-preserving learning by removing data privacy. Dataset distillation is also closely related to research topics including core-set selection, prototype generation, active learning, few-shot learning, generative models, and a broad area of learning from synthetic data.
Although DD has become an important paradigm in various machine-learning tasks, the potential of DD in computer vision (CV) applications, such as face recognition, person re-identification, and action recognition is far from being fully exploited.
Moreover, DD has rarely been demonstrated effectively in advanced computer vision tasks such as object detection, image segmentation, and video understanding.
Further, numerous unexplored challenges and unresolved issues exist in the realm of DD.
One such challenge pertains to finding efficient methods to modify existing DD workflows or create entirely new ones to address a wide range of computer vision tasks, extending beyond mere image classification.
An additional challenge lies in improving the scalability of dataset distillation (DD) methods to compress real-world datasets beyond the scale of ImageNet.
The purpose of this workshop is to unite researchers and professionals who share an interest in Dataset Distillation for computer vision for developing the next generation of dataset distillation methods for computer vision applications.
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As a result, research on designing state-of-the-art models gradually gets monopolized by large companies, while research groups with limited resources such as universities and small companies are unable to compete.
Reducing the training dataset size while preserving model training effects is significant for reducing the training cost, enabling green AI, and encouraging the university research groups to engage in the latest research.
This workshop focuses on the emerging research field of dataset distillation which aims to compress a large training dataset into a tiny informative one (e.g. 1\% of the size of the original data) while maintaining the performance of models trained on this dataset. Besides general-purpose efficient model training, dataset distillation can also greatly facilitate downstream tasks such as neural architecture/hyperparameter search by speeding up model evaluation, continual learning by producing compact memory, federated learning by reducing data transmission, and privacy-preserving learning by removing data privacy. Dataset distillation is also closely related to research topics including core-set selection, prototype generation, active learning, few-shot learning, generative models, and a broad area of learning from synthetic data.
Although DD has become an important paradigm in various machine-learning tasks, the potential of DD in computer vision (CV) applications, such as face recognition, person re-identification, and action recognition is far from being fully exploited.
Moreover, DD has rarely been demonstrated effectively in advanced computer vision tasks such as object detection, image segmentation, and video understanding.
Further, numerous unexplored challenges and unresolved issues exist in the realm of DD.
One such challenge pertains to finding efficient methods to modify existing DD workflows or create entirely new ones to address a wide range of computer vision tasks, extending beyond mere image classification.
An additional challenge lies in improving the scalability of dataset distillation (DD) methods to compress real-world datasets beyond the scale of ImageNet.
The purpose of this workshop is to unite researchers and professionals who share an interest in Dataset Distillation for computer vision for developing the next generation of dataset distillation methods for computer vision applications.
Second Workshop for Learning 3D with Multi-View Supervision
Abdullah J Hamdi
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7th Workshop on Autonomous Driving (WAD)
Vincent Casser
The CVPR 2024 Workshop on Autonomous Driving (WAD) brings together leading researchers and engineers from academia and industry to discuss the latest advances in autonomous driving. Now in its 7th year, the workshop has been continuously evolving with this rapidly changing field and now covers all areas of autonomy, including perception, behavior prediction and motion planning. In this full-day workshop, our keynote speakers will provide insights into the ongoing commercialization of autonomous vehicles, as well as progress in related fundamental research areas. Furthermore, we will host a series of technical benchmark challenges to help quantify recent advances in the field, and invite authors of accepted workshop papers to present their work.
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EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
Ronny Haensch
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2nd Workshop on Embodied "Humans": Symbiotic Intelligence between Virtual Humans and Humanoid Robots
Kwan-Yee Lin
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Data Curation and Augmentation in Enhancing Medical Imaging Applications
Shuoqi Chen
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Workshop on TDLCV: Topological Deep Learning for Computer Vision
Tolga Birdal
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Rhobin 2024: The second Rhobin challenge on Reconstruction of Human-Object Interaction
Xi Wang
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Ethical Considerations in Creative Applications of Computer Vision
Negar Rostamzadeh
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2nd Workshop and Challenge on DeepFake Analysis and Detection
Lorenzo Baraldi
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3rd Workshop on Vision Datasets Understanding and DataCV Challenge
Liang Zheng
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The Seventh International Workshop on Computer Vision for Physiological Measurement (CVPM)
Wenjin Wang
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Workshop on Graphic Design Understanding and Generation (GDUG)
Kota Yamaguchi
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VizWiz Grand Challenge: Describing Images and Videos Taken by Blind People
Danna Gurari
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Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture
Chris Padwick
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2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn)
Ruben Tolosana
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GAZE 2024: The 6th International Workshop on Gaze Estimation and Prediction in the Wild
Hyung Jin Chang
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LatinX in Computer Vision Research Workshop
Rodolfo Valiente Romero ⋅ Nils Murrugarra Llerena ⋅ Laura Montoya
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Third Workshop of Mobile Intelligent Photography & Imaging
Xiaoming Li
This workshop focuses on Mobile Intelligent and Photography Imaging (MIPI). It is closely connected to the impressive advancements of computational photography and imaging on mobile platforms (e.g., phones, AR/VR devices, and automatic cars), especially with the explosive growth of new image sensors and camera systems. Currently, the demand for developing and perfecting advanced image sensors and camera systems is rising rapidly. Meanwhile, new sensors and camera systems present interesting and novel research problems to the community. Moreover, the limited computing resources on mobile devices further compound the challenges, as it requires developing lightweight and efficient algorithms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging.
With the consecutive success of the 1st MIPI Workshop@ECCV 2022 and the 2nd MIPI Workshop@CVPR 2023, we will continue to arrange new sensors and imaging systems-related competition with industry-level data, and invite keynote speakers from both industry and academia to fuse the synergy. In this MIPI workshop, the competition will include three tracks: few-shot raw denoising, event-based sensor, and Nighttime Flare Removal. MIPI wishes to gather researchers and engineers together, encompassing the challenging issues and shaping future technologies in the related research directions.
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With the consecutive success of the 1st MIPI Workshop@ECCV 2022 and the 2nd MIPI Workshop@CVPR 2023, we will continue to arrange new sensors and imaging systems-related competition with industry-level data, and invite keynote speakers from both industry and academia to fuse the synergy. In this MIPI workshop, the competition will include three tracks: few-shot raw denoising, event-based sensor, and Nighttime Flare Removal. MIPI wishes to gather researchers and engineers together, encompassing the challenging issues and shaping future technologies in the related research directions.
The 3rd Explainable AI for Computer Vision (XAI4CV) Workshop
Indu Panigrahi
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2nd Workshop on ``What is Next in Multimodal Foundation Models?''
Rogerio Feris
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1st Workshop on Test-Time Adaptation: Model, Adapt Thyself! (MAT)
Evan Shelhamer
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5th Workshop on Continual Learning in Computer Vision (CLVISION)
Marc Masana
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ReGenAI: First Workshop on Responsible Generative AI
Adriana Romero-Soriano
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Data-Driven Autonomous Driving Simulation (DDASD)
Žan Gojčič ⋅ Maximilian Igl
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10th IEEE International Workshop on Computer Vision in Sports (CVsports)
Rikke Gade
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The 7th Workshop on Efficient Deep Learning for Computer Vision
Danny Yin
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XRNeRF: Second Workshop on Advances in Radiance Fields for the Metaverse
Aayush Prakash
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7th International Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues
Guoyu Lu
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IEEE International Workshop on Computational Cameras and Displays
M. Salman Asif
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FGVC11: 11th Workshop on Fine-grained Visual Categorization
Nico Lang
It may be tempting to think that image classification is a solved problem. However, one only needs to look at the poor performance of existing techniques in domains with limited training data and highly similar categories to see that this is not the case. In particular, fine-grained categorization, e.g., the precise differentiation between similar plant or animal species, disease of the retina, architectural styles, etc., is an extremely challenging problem, pushing the limits of both human and machine performance. In these domains, expert knowledge is typically required, and the question that must be addressed is how we can develop artificial systems that can efficiently discriminate between large numbers of highly similar visual concepts.
The 11th Workshop on Fine-Grained Visual Categorization (FGVC11) will explore topics related to supervised learning, self-supervised learning, semi-supervised learning, vision and language, matching, localization, domain adaptation, transfer learning, few-shot learning, machine teaching, multimodal learning (e.g., audio and video), 3D-vision, crowd-sourcing, image captioning and generation, out-of-distribution detection, anomaly detection, open-set recognition, human-in-the-loop learning, and taxonomic prediction, all through the lens of fine-grained understanding. Hence, the relevant topics are neither restricted to vision nor categorization.
Our workshop is structured around five main components:
(i) invited talks from world-renowned computer vision experts,
(ii) invited talks from experts in application domains (e.g., medical science and ecology),
(iii) interactive discussions during poster and panel sessions,
(iv) novel fine-grained challenges that are hosted as part of the workshop, and
(v) peer-reviewed extended abstract paper submissions.
We aim to stimulate debate and to expose the wider computer vision community to new and challenging problems in areas that have the potential for large societal impact but do not traditionally receive a significant amount of exposure at other CVPR workshops.
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The 11th Workshop on Fine-Grained Visual Categorization (FGVC11) will explore topics related to supervised learning, self-supervised learning, semi-supervised learning, vision and language, matching, localization, domain adaptation, transfer learning, few-shot learning, machine teaching, multimodal learning (e.g., audio and video), 3D-vision, crowd-sourcing, image captioning and generation, out-of-distribution detection, anomaly detection, open-set recognition, human-in-the-loop learning, and taxonomic prediction, all through the lens of fine-grained understanding. Hence, the relevant topics are neither restricted to vision nor categorization.
Our workshop is structured around five main components:
(i) invited talks from world-renowned computer vision experts,
(ii) invited talks from experts in application domains (e.g., medical science and ecology),
(iii) interactive discussions during poster and panel sessions,
(iv) novel fine-grained challenges that are hosted as part of the workshop, and
(v) peer-reviewed extended abstract paper submissions.
We aim to stimulate debate and to expose the wider computer vision community to new and challenging problems in areas that have the potential for large societal impact but do not traditionally receive a significant amount of exposure at other CVPR workshops.
ScanNet++ Novel View Synthesis and 3D Semantic Understanding Challenge
Angela Dai
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New frontiers for zero-shot Image Captioning Evaluation (NICE)
Taehoon Kim
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The Sixth Workshop on Deep Learning for Geometric Computing (DLGC 2024)
Dena Bazazian
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4th Workshop and Challenge on Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings
Iro Armeni
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L3D-IVU: 3rd Workshop on Learning with Limited Labelled Data for Image and Video Understanding
Valentina Zantedeschi
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The First Workshop on the Evaluation of Generative Foundation Models
Maria Zontak
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Representation Learning with Very Limited Images: Zero-shot, Unsupervised, and Synthetic Learning in the Era of Big Models
Hirokatsu Kataoka
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Learning from Procedural Videos and Language: What is Next?
Effrosyni Mavroudi
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5th Workshop on Robot Visual Perception in Human Crowded Environments
Hengcan Shi
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6th Workshop and Competition on Affective Behavior Analysis in-the-wild
Dimitrios Kollias
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New Trends in Multimodal Human Action Perception, Understanding and Generation
Yansong Tang
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AVA: Accessibility, Vision and Autonomy Meet
Eshed Ohn-Bar ⋅ Danna Gurari ⋅ Chieko Asakawa ⋅ Hernisa Kacorri ⋅ Kris Kitani ⋅ Jennifer Mankoff
The goal of this workshop is to gather researchers, students, and advocates who work at the intersection of accessibility, computer vision, and autonomous and intelligent systems. In particular, we plan to use the workshop to identify challenges and pursue solutions for the current lack of shared and principled development tools for vision-based accessibility systems. For instance, there is a general lack of vision-based benchmarks and methods relevant to accessibility (e.g., people using mobility aids are currently mostly absent from large-scale datasets in pedestrian detection). Towards building a community of accessibility-oriented research in computer vision conferences, we also introduce a large-scale fine-grained computer vision challenge. The challenge involves visual recognition tasks relevant to individuals with disabilities. We aim to use the challenge to uncover research opportunities and spark the interest of computer vision and AI researchers working on more robust and broadly usable visual reasoning models in the future. An interdisciplinary panel of speakers will further provide an opportunity for fostering a mutual discussion between accessibility, computer vision, and robotics researchers and practitioners.
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EgoMotion: Egocentric Body Motion Tracking, Synthesis and Action Recognition
Lingni Ma
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OpenSUN3D: 2nd Workshop on Open-Vocabulary 3D Scene Understanding
Francis Engelmann
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