# CSE5519 Advances in Computer Vision (Topic B: 2025: Vision-Language Models)

## Molmo and PixMo:

[link to paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Deitke_Molmo_and_PixMo_Open_Weights_and_Open_Data_for_State-of-the-Art_CVPR_2025_paper.pdf)

## Novelty in Molmo and PixMo

PixMo dataset (712k images with long 200+ words description)

- Simplified two-stage training pipline
  - Standard ViT architecture with tokenizer and image encoder (CLIP) and pooling the embeddings to the decoder only LLM.
- overlapping multi-crop policy
  - Add overlapping region and image cropping to truncate the large image.
- training over multiple annotations
  - Text-only residual dropout
- optimizer setups

> [!TIP]
>
> This paper provides an interesting dataset and a refined training pipeline that is comparable to current closed-source SOTA performance. What is the contribution of the paper from the algorithm perspective? It seems that it is just a test for a new dataset with a slightly altered training pipeline.
