L3D-IVU CVPR2024
3rd Workshop on Learning with Limited Labelled Data for Image and Video Understanding
18 June 2024
Location: Seattle Convention Center
motivation
Deep learning has been widely successful in a variety of computer vision tasks such as object recognition, object detection, and semantic segmentation. It also has been deployed with success in learning spatiotemporal features for video segmentation/detection and action recognition tasks. However, one of the major bottlenecks of deep learning in both image and video understanding tasks is the need for large-scale labelled datasets. Collecting and annotating such datasets can be labor intensive and costly. In many scenarios of practical interest only a few labelled examples of novel categories may be available at model training time. Currently available large-scale data typically cover relatively narrow sets of categories and are constrained by licensing. As such, they are often hard to naively apply to practical problems. It is especially problematic in developing countries that do not have the required resources to collect large scale labelled datasets for new tasks. The goal of this workshop is to explore approaches that learn from limited labelled data, or with side information such as text data, or using data with weak/self supervision, with special focus on video understanding tasks. This will be the third L3D-IVU workshop in conjunction with CVPR, where it had a great success and wide interest from multiple researchers as it explores the intersection of learning with limited labelled data and video understanding. This year, our workshop's theme will be around learning with limited labelled data and AI for social good, where we discuss assistive technologies and remote sensing amongst others.
News and Updates:
Jan 10, 2024: Workshop Officially Confirmed for CVPR 2024, that will take place on Tuesday June 18, 2024.
Jan 21, 2024: Paper Submission is open at: https://cmt3.research.microsoft.com/L3DIVUCVPR2024/.
Feb 5, 2024: Challenge details on OpenEarthMap Land Cover Mapping Few-Shot released.
March 3, 2024: Submission deadline has been extended till March 11th at 11:59 pm.
Challenge Prizes:
1000$ (500$ ServiceNow + 500$ IVU Lab, PI Mennatullah Siam)
Best Paper prize:
1000$ (IPad Pro)
(Only students/researchers are eligible to participate. Government officials, public sector officials, and employees of entities who do business in the Public Sector are not eligible to participate. The sponsors reserve the right not to make the award if the recipient turns out to be a government official, public sector official, or individual who does business in the public sector.)
Participation:
We encourage submissions that are under one of the topics of interest, but also we welcome other interesting and relevant research for learning with limited labeled data.
Few-Shot classification, detection and segmentation in still images and video, including objects, actions, scenes and object tracking.
Zero-shot learning in video understanding.
Video and language modelling.
Self supervised Learning in video related tasks.
Weakly/Semi supervised learning in video understanding.
Transfer Learning.
Open-set learning.
New benchmarks and metrics.
Real-world applications discussing the societal impact of few-shot learning.
Papers will be peer-reviewed under a double-blind policy and the submission deadline is the 6th of March 2024. Accepted papers will be presented at the poster session, some as orals, and one paper will be awarded as the best paper.
Invited Speakers
Eleni Triantafillou
Google Brain
Stella Yu
University of Michigan
Alaa El-Nouby
Apple
Ming-Hsuan Yang
University of California Merced
Raoul de Charette
Inria Paris
Daniela Massiceti
Microsoft Research
Cees G. M. Snoek
University of Amsterdam
