How does the performance of MMICL's zero-shot image-text retrieval compare to Flamingo, PaLI, and BLIVA on the

Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utilize extensive background knowledge and task information with in-context learning, most VLMs still struggle with understanding complex multi-modal prompts with multiple images, making VLMs less effective in downstream vision-language tasks. In this paper, we address the limitation above by 1) introducing vision-language Model with Multi-Modal In-Context Learning(MMICL), a new approach to allow the VLM to deal with multi

Research goal: How does the performance of MMICL's zero-shot image-text retrieval compare to Flamingo, PaLI, and BLIVA on the SBU Captions dataset when using a fixed number of in-context examples?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.