Workshops

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

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.

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.

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.

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.

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.