Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality
Kunal Pratap Singh, Ali Garjani, Rishubh Singh, Muhammad Uzair Khattak, Efe Tarhan, Jason Toskov, Andrei Atanov, O\u{g}uzhan Fatih Kar, Amir Zamir
Why It Matters
What makes this one worth your time
This research could significantly enhance the efficiency of training models for specific applications, particularly in robotics, by minimizing the need for extensive pre-training on diverse datasets.
Introducing Test-Space Training for specialized multimodal learning in constrained environments.
Summary
The paper proposes a method called Test-Space Training (TST) that utilizes cross-modal learning to specialize models for specific test environments using multimodal data collected from those environments, aiming to reduce reliance on large-scale external datasets.
Key contributions
- Development of Test-Space Training (TST) for multimodal data collection and self-supervised pre-training.
- Empirical evaluation of models specialized for test environments against generalist models.
- Analysis of the impact of modality substitution on model performance.
Notable insights
- The approach leverages insights from developmental psychology to inform model training strategies.
- The tradeoff between specialization and generalization in model training is explored through varying pre-training data.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.14721v1 Announce Type: cross 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.