AI BriefingLG AI Research 247
Key point
Proposes a new methodology that improves Domain Generalization (DG) performance by using Natural Language Supervision to align visual representations with human reasoning.
Details
Machine learning models face the problem of Domain Generalization (DG), where performance drops sharply when the distribution of training data differs from that of test data. For example, a car classification model trained on urban environments may not work properly on sketches or images of rural environments.
This study proposes a method of combining visual representations with text that embodies typical human reasoning. Rather than simply relying on image labeling, it leverages Textual Explanation that reflects the human thought process, guiding the model to learn richer semantic information.
To achieve this, two key modules are introduced:
- Visual and Textual Joint Embedder: Aligns visual representations with pivot sentence embeddings.
- Textual Explanation Generator: Generates text explaining the logical reasons underlying a decision.
The proposed methodology achieves state-of-the-art (SOTA) performance on both the newly constructed CUB-DG dataset and the DomainBed benchmark, demonstrating that it is possible to build models that are robust to domain shifts.
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