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Workshops
Workshop
Auke Wiggers · Amirhossein Habibian

[ Summit 420-422 ]

Abstract
Workshop
Zheng Tang

[ Summit 444 ]

Abstract
Workshop
Radu Timofte · Zongwei Wu

[ Arch 204 ]

Abstract

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

Workshop
Rakesh Ranjan

[ Summit 332 ]

Abstract
Workshop
Khoi Nguyen

[ Summit 423-425 ]

Abstract
Workshop
Anoop Cherian

[ Summit 320 ]

Abstract
Workshop
Felix Juefei Xu · Tingbo Hou

[ Summit 432 ]

Abstract
Workshop
Mohsen Fayyaz

[ Summit 429 ]

Abstract
Workshop
Hisham Cholakkal · Teng Xi

[ Summit 434 ]

Abstract
Workshop
David Fouhey

[ Arch 2A ]

Abstract
Workshop
Marcos V. Conde

[ Arch 3A ]

Abstract
Workshop
Saeed Vahidian

[ Summit 329 ]

Abstract

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, …

Workshop
Shaodi You

[ Summit 333 ]

Abstract
Workshop
Jun Ma

[ Summit 324 ]

Abstract
Workshop
Andrey Ignatov

[ Arch 211 ]

Abstract
Workshop
Chunyuan Li

[ Arch 3B ]

Abstract
Workshop
Yuhao Chen

[ Arch 309 ]

Abstract
Workshop
Jack Langerman · Ruisheng Wang

[ Summit 443 ]

Abstract
Workshop
Srikrishna Karanam

[ Arch 303 ]

Abstract
Workshop
Mei Chen

[ Arch 304 ]

Abstract
Workshop
Jun Wan

[ Arch 201 ]

Abstract
Workshop
Ziad Al-Halah

[ Summit 334 ]

Abstract
Workshop
Despoina Paschalidou

[ Arch 4F ]

Abstract
Workshop
James Tompkin · Deqing Sun

[ Summit 342 ]

Abstract
Workshop
Patrick Wenzel

[ Summit 327 ]

Abstract
Workshop
Qianli Ma

[ Summit 420-422 ]

Abstract
Workshop
Antonino Furnari

[ Summit 428 ]

Abstract
Workshop
Latha Pemula

[ Summit 330 ]

Abstract
Workshop
Azade Farshad

[ Summit 322 ]

Abstract
Workshop
Ahmet Iscen

[ Summit 321 ]

Abstract
Workshop
Abdullah J Hamdi

[ Summit 331 ]

Abstract
Workshop
Vincent Casser

[ Summit 345-346 ]

Abstract

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.

Workshop
Tat-Jun Chin

[ Arch 205 ]

Abstract
Workshop
Efstratios Gavves

[ Arch 210 ]

Abstract
Workshop
Andrew Owens

[ Summit 326 ]

Abstract
Workshop
Bir Bhanu

[ Arch 203 ]

Abstract
Workshop
Li Chen

[ Summit 442 ]

Abstract
Workshop
Amir Bar · Kaiyang Zhou

[ Summit 335-336 ]

Abstract
Workshop
Ronny Haensch

[ Arch 310 ]

Abstract
Workshop
Anand Bhattad

[ Arch 201 ]

Abstract
Workshop
Tolga Birdal

[ Summit 328 ]

Abstract
Workshop
Shuoqi Chen

[ Summit 347-348 ]

Abstract
Workshop
Sam Tsai

[ Summit 423-425 ]

Abstract
Workshop
Eduard Trulls

[ Summit 323 ]

Abstract
Workshop
Henghui Ding

[ Summit 429 ]

Abstract
Workshop
Kota Yamaguchi

[ Summit 344 ]

Abstract
Workshop
Changan Chen

[ Arch 2B ]

Abstract
Workshop
Vidya Narayanan

[ Arch 309 ]

Abstract
Workshop
Fangneng Zhan

[ Summit 332 ]

Abstract
Workshop
Lorenzo Baraldi

[ Arch 205 ]

Abstract
Workshop
Negar Rostamzadeh

[ Arch 213 ]

Abstract
Workshop
Liang Zheng

[ Summit 436 ]

Abstract
Workshop
Stephen McGough

[ Summit 420 - 422 ]

Abstract
Workshop
Gedas Bertasius

[ Summit 347-348 ]

Abstract
Workshop
Ruben Tolosana

[ Arch 212 ]

Abstract
Workshop
Alexei Skurikhin

[ Arch 214 ]

Abstract
Workshop
Anyi Rao

[ Summit 343 ]

Abstract
Workshop
Danny Yin

[ Summit 420-422 ]

Abstract
Workshop
Candice Schumann

[ Arch 303 ]

Abstract
Workshop
Habib Slim

[ Summit 327 ]

Abstract
Workshop
Evan Shelhamer

[ Summit 324 ]

Abstract
Workshop
Guy Tevet

[ Summit 430 ]

Abstract
Workshop
Jieyu Zhang

[ Summit 423-425 ]

Abstract
Workshop
Ehud Barnea

[ Arch 310 ]

Abstract
Workshop
Sachini A Herath

[ Arch 201 ]

Abstract
Workshop
Rogerio Feris

[ Summit 437-439 ]

Abstract
Workshop
Rodolfo Valiente Romero · Nils Murrugarra Llerena · Laura Montoya

[ Arch 203 ]

Abstract
Workshop
Congyue Deng

[ Summit 321 ]

Abstract
Workshop
Adriana Romero-Soriano

[ Summit 433 ]

Abstract
Workshop
Mei Chen

[ Summit 431 ]

Abstract
Workshop
Xiaoming Li

[ Arch 213 ]

Abstract

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 …

Workshop
Žan Gojčič · Maximilian Igl

[ Summit 342 ]

Abstract
Workshop
Davide Moltisanti

[ Summit 335-336 ]

Abstract
Workshop
Lei Zhang

[ Summit 329 ]

Abstract
Workshop
Aayush Prakash

[ Summit 332 ]

Abstract
Workshop
Shu Kong

[ Summit 328 ]

Abstract
Workshop
Indu Panigrahi

[ Arch 2A ]

Abstract
Workshop
Adam Kortylewski

[ Summit 432 ]

Abstract
Workshop
Marc Masana

[ Summit 325 ]

Abstract
Workshop
Kaavya Rekanar

[ Arch 205 ]

Abstract
Workshop
Nico Lang

[ Summit 326 ]

Abstract

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 …

Workshop
Hany Farid

[ Arch 2B ]

Abstract
Workshop
M. Salman Asif

[ Arch 204 ]

Abstract
Workshop
Anthony G Francis

[ Summit 428 ]

Abstract
Workshop
Hao Su

[ Summit 434 ]

Abstract
Workshop
Boyi Li

[ Summit 345-346 ]

Abstract
Workshop
Paolo Rota

[ Summit 320 ]

Abstract
Workshop
Timo Saemann

[ Arch 304 ]

Abstract
Workshop
Taehoon Kim

[ Summit 323 ]

Abstract
Workshop
Dena Bazazian

[ Summit 448 ]

Abstract
Workshop
Julieta Martinez

[ Summit 333 ]

Abstract
Workshop
Maria Zontak

[ Summit 433 ]

Abstract
Workshop
Matthew A Gwilliam

[ Summit 335-336 ]

Abstract
Workshop
Matteo Poggi · Matteo Poggi

[ Summit 331 ]

Abstract
Workshop
Dimitrios Kollias

[ Arch 212 ]

Abstract
Workshop
Khoa Luu

[ Summit Elliott Bay ]

Abstract
Workshop
Riad I. Hammoud

[ Arch 201 ]

Abstract
Workshop
Effrosyni Mavroudi

[ Summit 427 ]

Abstract
Workshop
Xiaowei Zhou

[ Summit 332 ]

Abstract
Workshop
Eshed Ohn-Bar · Danna Gurari · Chieko Asakawa · Hernisa Kacorri · Kris Kitani · Jennifer Mankoff

[ Summit 435 ]

Abstract

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.

Workshop
Francis Engelmann

[ Arch 211 ]

Abstract
Workshop
Tse-Wei Chen

[ Arch 205 ]

Abstract