---
license: mit
tags:
- vision
- vision-language-model
- contrastive learning
---

FLAIR Model

Authors: [Rui Xiao](https://www.eml-munich.de/people/rui-xiao), [Sanghwan Kim](https://kim-sanghwan.github.io/), [Mariana-Iuliana Georgescu](https://lilygeorgescu.github.io/), [Zeynep Akata](https://www.eml-munich.de/people/zeynep-akata), [Stephan Alaniz](https://www.eml-munich.de/people/stephan-alaniz)

FLAIR was introduced in the paper [FLAIR: VLM with Fine-grained Language-informed Image Representations](https://arxiv.org/abs/2412.03561). Based on ViT-B-16 Model from [OpenCLIP](https://github.com/mlfoundations/open_clip), FLAIR features text-conditioned attention pooling at the end of its vision transformer. Pre-trained on MLLM-recaptioned datasets from [DreamLIP](https://huggingface.co/datasets/qidouxiong619/dreamlip_long_captions), FALIR achieves strong performance in tasks such as zero-shot image-text retrieval and zero-shot segmentation.