COMPUTER VISION

ML APPLICATIONS

Self-Supervised Learning of Pretext-Invariant Representations

June 09, 2020

Abstract

The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as ``pearl'') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised learning of image representations with good invariance properties.

Download the Paper

AUTHORS

Publisher

CVPR

Research Topics

Computer Vision

Related Publications

June 11, 2025

ROBOTICS

COMPUTER VISION

CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models

Aaron Foss, Chloe Evans, Sasha Mitts, Koustuv Sinha, Ammar Rizvi, Justine T. Kao

June 11, 2025

June 11, 2025

RESEARCH

COMPUTER VISION

IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments

Florian Bordes, Quentin Garrido, Justine Kao, Adina Williams, Mike Rabbat, Emmanuel Dupoux

June 11, 2025

June 11, 2025

RESEARCH

COMPUTER VISION

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs

Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran

June 11, 2025

June 11, 2025

ROBOTICS

RESEARCH

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas

June 11, 2025

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.