VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

TL;DR

This paper discusses the fourth edition of a workshop focused on developing data-efficient deep learning methods for computer vision, emphasizing the use of inductive priors and novel approaches to improve performance with limited data.

Contribution

It introduces new challenge tasks that promote the development of models with inductive biases, leading to significant improvements over baselines without transfer learning.

Findings

Winning solutions outperform baselines significantly.

Heavy data augmentation and large ensembles are common strategies.

Novel prior-based methods contribute more than previous editions.

Abstract

The fourth edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop features two data-impaired challenges. These challenges address the problem of training deep learning models for computer vision tasks with limited data. Participants are limited to training models from scratch using a low number of training samples and are not allowed to use any form of transfer learning. We aim to stimulate the development of novel approaches that incorporate inductive biases to improve the data efficiency of deep learning models. Significant advancements are made compared to the provided baselines, where winning solutions surpass the baselines by a considerable margin in both tasks. As in previous editions, these achievements are primarily attributed to heavy use of data augmentation policies and large model ensembles, though novel prior-based methods seem to contribute more to successful solutions compared to last year. This report highlights the key aspects of the challenges and their outcomes.