Multi-modal Generation Via Cross-modal In-context Learning

Kumar Amandeep, Naseer Muzammal, Narayan Sanath, Anwer Rao Muhammad, Khan Salman, Cholakkal Hisham. Arxiv 2024

[Paper] [Code]    
Has Code In Context Learning Merging Multimodal Models Prompting RAG Reinforcement Learning

In this work, we study the problem of generating novel images from complex multimodal prompt sequences. While existing methods achieve promising results for text-to-image generation, they often struggle to capture fine-grained details from lengthy prompts and maintain contextual coherence within prompt sequences. Moreover, they often result in misaligned image generation for prompt sequences featuring multiple objects. To address this, we propose a Multi-modal Generation via Cross-Modal In-Context Learning (MGCC) method that generates novel images from complex multimodal prompt sequences by leveraging the combined capabilities of large language models (LLMs) and diffusion models. Our MGCC comprises a novel Cross-Modal Refinement module to explicitly learn cross-modal dependencies between the text and image in the LLM embedding space, and a contextual object grounding module to generate object bounding boxes specifically targeting scenes with multiple objects. Our MGCC demonstrates a diverse range of multimodal capabilities, like novel image generation, the facilitation of multimodal dialogue, and generation of texts. Experimental evaluations on two benchmark datasets, demonstrate the effectiveness of our method. On Visual Story Generation (VIST) dataset with multimodal inputs, our MGCC achieves a CLIP Similarity score of \(0.652\) compared to SOTA GILL \(0.641\). Similarly, on Visual Dialogue Context (VisDial) having lengthy dialogue sequences, our MGCC achieves an impressive CLIP score of \(0.660\), largely outperforming existing SOTA method scoring \(0.645\). Code: https://github.com/VIROBO-15/MGCC

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