Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality

2026-07-16

AI Summary

This study addresses the challenge of enabling models to learn efficiently and achieve strong performance in a specific test environment without relying on large-scale external data. The authors propose Test-Space Training (TST), a novel approach that systematically explores the feasibility of cross-modal self-supervised pretraining using only multimodal sensor data collected from devices within the target test environment. By leveraging multimodal alignment and environment-specific modeling, TST substantially reduces dependence on massive internet-scale datasets and demonstrates that modality diversity can partially compensate for limited data scale. Experimental results show that models trained exclusively on in-environment data attain performance on multiple downstream tasks comparable to that of prominent models such as DINOv2 and CLIP, which are pretrained on vast generic datasets.

Abstract

Cross-modal learning, i.e., learning to predict one modality from another, is a fundamental mechanism for self-supervision via leveraging multimodality. Many practical applications, e.g., deploying a household robot, involve devices that are equipped with a rich set of sensors that enable multimodal sensing in their test environment. This presents an opportunity to apply cross-modal learning to the multimodal data sensed by these devices to learn representations. Findings in developmental psychology also suggest that biological agents leverage it to build an effective representation of their surroundings. To study this, we propose a controlled setup, where we restrict a user device to just a given test environment. It results in a specialization setup where we attempt to develop a performant model for this specific test environment. Under this setup, we develop Test-Space Training (TST), which performs multimodal data collection in the test environment and performs self-supervised pre-training on it. We evaluate these models on various downstream tasks in the same environment. Under this setup, we find various interesting insights, such as collecting rich multimodal data only from the test environment and leveraging cross-modal learning, we can achieve competitive results with generalist models (e.g., DINOv2 and CLIP) pre-trained on large-scale internet datasets. This enables an alternative scenario where the need for external Internet-scale datasets for pre-training models is reduced. We also present a set of analyses and ablations that raise intriguing points on substituting data with (multi)modality, and how varying pre-training data enables a tradeoff between a model's abilities to specialise to a test environment, and generalize to held-out spaces.

Problem

• multimodality
• self-supervised learning
• test environment specialization
• cross-modal learning
• data efficiency

Innovation

• multimodal self-supervision
• test-time specialization
• cross-modal learning
• Test-Space Training
• modality-as-supervision