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GPT4RoI: Advancing Vision-Language Models through Instruction-Tuned Region-Text Pairs

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Unleashing the Potential of Large Language Models

Large language models like ChatGPT, Claude, Bard, and GPT-4 have showcased remarkable capabilities in conversational natural language processing. These models, including community opensource projects like LLama, Alpaca, Vicuna, ChatGLM, MOSS, offer a potential avenue towards developing general-purpose AI models.

Aligning Vision-Language Models through Instruction Tuning

To bridge the gap between vision and language, researchers have explored vision-language models such as MiniGPT-4, LLaVA, LLaMA-Adapter, InstructBLIP, etc. These models align the vision encoder with LLM through instruction tuning on image-text pairings. However, their region-level alignment limits their performance on complex comprehension tasks.

A Holistic Approach to Vision-Language Models

GPT4RoI aims to overcome the limitations of existing vision-language models by providing fine-grained comprehension of regions of interest. Unlike previous approaches, GPT4RoI develops a vision-language model from scratch to achieve comprehensive region-level understanding. This end-to-end design enhances the versatility of multimodal models for various tasks.

Spatial Instruction for Fine-Grained Comprehension

GPT4RoI adopts the object box as the format for spatial instruction, enabling the model to refer to specific parts of an image. By supplying the extracted visual elements and linguistic instructions, GPT4RoI utilizes spatial instruction to generate accurate responses. The model is trained on region-text datasets, combining publicly accessible datasets like COCO, RefCOCO, Flickr30K, Visual Genome, and Visual Commonsense Reasoning.

Enhanced Conversational Quality and Human-Like Replies

GPT4RoI benefits from carefully selected image-text datasets, leading to improved conversational quality and more human-like responses. By incorporating information on item categories and basic characteristics, the model can pre-train the region feature extractor without affecting the LLM. Lengthier texts with complex ideas enable end-to-end fine-tuning, simulating real-world user instructions.

Expanding Capabilities and Interactive Experience

GPT4RoI unlocks new abilities beyond image-level comprehension, including intricate area reasoning and region captioning. The integration of spatial instruction tuning offers users a unique interactive experience, allowing them to communicate with the model using both language and spatial instructions. The model’s code, instruction tuning format for datasets, and an online demo are available on GitHub.


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