Impact of Denoising Diffusion Processes on Zero-Shot Instruction Following Accuracy in Multimodal Models

Abstract

Abstract: We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-language alignment, we unfreeze the large language model (LLM) during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, to enable multi-purpose capabilities, we mix a variety of tasks for joint visual instruction tuning, and

Research Question

How does replacing GAN-based layout priors with denoising diffusion processes impact the zero-shot instruction following accuracy of multimodal models on the RefCOCO+ visual grounding benchmark?

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