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Meta's FLAT for Multimodal Understanding and Generation
Meta's FLAT method converts multimodal data into continuous token sequences, allowing models to scale visual detail based on available compute.
What: Meta AI introduced FLAT (Flexible-Length Adaptation of Tokens), which uses nested dropout to allow models to dynamically trade computational resources for varying levels of image resolution.
Why it matters: This technique provides a way to make multimodal models more efficient by letting them adapt their input processing to the complexity of the task, rather than forcing a fixed resolution for all images.
Decoder
- Nested dropout: A regularization technique that organizes model information in a hierarchy, allowing parts of the model to be 'dropped' during inference to reduce compute costs while maintaining performance.
Original article
Meta AI introduced FLAT, a method that converts images and text into the same flexible-length sequence of continuous tokens for retrieval and generation. Nested dropout arranges information from coarse to fine, letting models trade computation for visual detail by varying the number of tokens used.
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