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The training strategy of the proposed IMI-VL model. Stage 1: Training dataset construction involves generating textual responses and image descriptions through a language model and a vision-language model. These are combined into interleaved responses using image contexts and captions. Stage 2: Supervised fine-tuning refines the model with a vision encoder, adapter, and language model, optimizing through generative loss.
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Vision-Language Models (VLMs) have demonstrated remarkable capabilities in understanding multimodal inputs and have been widely integrated into Retrieval-Augmented Generation (RAG) based conversational systems. While current VLM-powered chatbots can provide textual source references in their responses, they exhibit significant limitations in refere...
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