$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization
arXiv:2510.11296v3 Announce Type: replace-cross Abstract: Recent approaches for vision-language models (VLMs) have shown remarkable success in achieving fast downstream adaptation. When applied to real-world downstream tasks, VLMs inevitably encounter both the in-distribution (ID) data and out-of-distribution (OOD) data. The OOD datasets often include both covariate shifts (e.g., known classes with changes in image styles) and semantic shifts (e.g., test-time unseen classes). This highlights the importance of improving VLMs' generalization ability to covariate-shifted OOD data, while effective
This paper addresses a critical challenge in the deployment of advanced AI models, particularly as vision-language models become more prevalent and encounter diverse real-world data distributions.
Improving OOD generalization and detection is crucial for the reliability, safety, and commercial viability of AI systems, especially in high-stakes applications.
This research suggests a methodology that could make VLMs more robust to unforeseen data variations, enhancing their practical applicability beyond controlled environments.
- · AI developers
- · Autonomous systems integrators
- · Industries deploying AI in variable environments
- · AI solutions with poor OOD robustness
- · Legacy machine learning models
More robust and deployable AI systems across various industries.
Accelerated adoption of advanced AI in fields requiring high reliability, such as healthcare, automotive, and defense.
Enhanced trust in AI systems leading to broader societal integration and new applications currently deemed too risky.
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Read at arXiv cs.LG