Researchers are exploring the adaptation of decoder-only Transformers like LLaMA to the computer vision field. Vision-language models (VLMs) combining vision encoders with language models are being studied for their performance on tasks, with some observations suggesting limited improvement over language models.
We've made it easier to fine-tune VLMs like LLaVa, check out the tutorial below for more info! The `SFTTrainer` class of TRL now includes experimental support for fine-tuning vision-language models on custom data :) https://t.co/hH4441EaAk
Ever wanted to learn about fantastic vision language models and how to find and fine-tune them? 🧙🏻 We've just added support to train VLMs like LLaVa in TRL and wrote a walkthrough on vision language models! 🎉 Read about VLMs and SFTTrainer for vision https://t.co/j2QvXAcWLV https://t.co/3sIy8exuE6
I have been working on vision+language models (VLMs) for a decade. And every few years, this community re-discovers the same lesson -- that on difficult tasks, VLMs regress to being nearly blind! Visual content provides minor improvement to a VLM over an LLM, even when these… https://t.co/StilR2HbyO https://t.co/zODnxJeZ8p
BRAVE Broadening the visual encoding of vision-language models Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, https://t.co/CHnKDFc5VW
Adapting LLaMA Decoder to Vision Transformer This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We first "LLaMAfy" a standard ViT step-by-step https://t.co/XugxNfnrgl