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Found 5 papers, 2 papers with code
VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework
With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question.
VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding
It seamlessly integrates various SOTA vision models and brings the automation in the selection of SOTA vision models, identifies the suitable 3D mesh creation algorithms corresponding to 2D depth maps analysis, generates optimal results based on diverse multimodal inputs such as text prompts.
WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs
Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content.
UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework
In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
