[R] COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

**Paper**: [https://arxiv.org/abs/2401.00849](https://arxiv.org/abs/2401.00849)

**Code**: [https://github.com/showlab/cosmo](https://github.com/showlab/cosmo)

**Models**: [https://huggingface.co/Awiny](https://huggingface.co/Awiny)

**Dataset**: [https://huggingface.co/datasets/Awiny/Howto-Interlink7M](https://huggingface.co/datasets/Awiny/Howto-Interlink7M)

**Project page**: [https://fingerrec.github.io/cosmo/](https://fingerrec.github.io/cosmo/)

**Abstract**:

>In the evolution of Vision-Language Pre-training, shifting from short-text comprehension to encompassing extended textual contexts is pivotal. Recent autoregressive vision-language models like \[Flamingo, PaLM-E\], leveraging the long-context capability of Large Language Models, have excelled in few-shot text generation tasks but face challenges in alignment tasks. Addressing this gap, we introduce the contrastive loss into text generation models, presenting the COntrastive-Streamlined MultimOdal framework (**CosMo**), strategically partitioning the language model …

abstract alignment capability challenges context evolution face few-shot gap language language models large language large language models loss machinelearning palm palm-e pivotal presenting pre-training tasks text text generation textual training vision vision-language models