BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs (2307.08581v1)

Abstract: LLMs have demonstrated remarkable abilities at interacting with humans through language, especially with the usage of instruction-following data. Recent advancements in LLMs, such as MiniGPT-4, LLaVA, and X-LLM, further enlarge their abilities by incorporating multi-modal inputs, including image, video, and speech. Despite their effectiveness at generating precise and detailed language understanding of the given modality signal, these LLMs give up the ability to ground specific parts of inputs, thus only constructing a coarse-grained mapping. However, explicit and informative correspondence between text and other modalities will not only improve the user experience but also help to expand the application scenario of multi-modal LLMs. Therefore, we propose BuboGPT, a multi-modal LLM with visual grounding that can perform cross-modal interaction between vision, audio and language, providing fine-grained understanding of visual objects and other given modalities. As a result, BuboGPT is able to point out the specific location of an object in the image, when it is generating response or description for that object. Our contributions are two-fold: 1) An off-the-shelf visual grounding module based on SAM that extracts entities in a sentence and find corresponding masks in the image. 2) A two-stage training scheme and instruction dataset to endow joint text-image-audio understanding. Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human. It performs consistently well when provided by arbitrary modality combinations (either aligned or unaligned). Our code, model and dataset are available at https://bubo-gpt.github.io .

An Overview of BuboGPT: Visual Grounding in Multi-Modal LLMs

The paper presents BuboGPT, a LLM designed for multi-modal understanding incorporating vision, audio, and language. Unlike previous models which focused on coarse-grained mapping, BuboGPT introduces visual grounding, enabling the model to explicitly associate text with specific visual objects, thus enhancing the application potential of multi-modal LLMs.

Key Contributions

BuboGPT introduces two primary innovations:

1. Visual Grounding Module: Utilizing a combination of semantic segmentation and state-of-the-art visual recognition models, BuboGPT establishes a fine-grained correspondence between entities in text and visual inputs.
2. Two-Stage Training Framework: The model undergoes a preliminary alignment with image and audio datasets followed by a refined multi-modal instruction tuning. This strategy facilitates the model's ability to process varied modality inputs and generate coherent language outputs.

Methodology

Visual Grounding Pipeline: The system employs a tagging module to identify relevant visual entities and a grounding module to associate these with semantic masks in the image. An entity-matching component further refines the alignment between these visual entities and corresponding textual descriptions, leveraging LLMs for reasoning.

Training Process:

• Stage 1: Aligns the vision and audio encoders with language outputs using datasets containing image-text and audio-text pairs.
• Stage 2: Utilizes a specially curated instruction-following dataset to enable the model to process and correlate image, audio, and text inputs effectively.

Experimental Findings

The results reveal BuboGPT’s proficiency in visual grounding, even with complex and arbitrary inputs. The model demonstrates thorough understanding and interaction across modalities, affirming its capability to handle both aligned and unaligned inputs. This includes its performance in visually grounding text descriptions to specific objects within images.

Implications and Future Directions

BuboGPT addresses a notable gap in multi-modal LLMs by introducing visual grounding capabilities. The implications are manifold, notably enriching user interaction experiences and expanding potential application domains in AI-driven fields such as education, accessibility, and content generation.

Future research may focus on enhancing the grounding QA capacities, countering language hallucinations, and expanding datasets for more diverse multi-modal integrations. Addressing these challenges could further tighten the alignment between language and other modalities, thus pushing the boundaries of multi-modal AI systems.

Through these advancements, BuboGPT positions itself as a significant contributor to the evolution of multi-modal LLMs, providing a robust framework for future explorations into fine-grained multi-modal understanding.