Enhancing Fine-Grained Image Perception in Multi-modal LLMs with Referential Comprehension

מושגי ליבה

Enhancing fine-grained image perception in multi-modal Large Language Models (LLMs) through instruction tuning and referential comprehension.

תקציר

The paper proposes a new framework to improve the fine-grained image understanding abilities of Multi-modal Large Language Models (MLLMs). By constructing an instruction tuning dataset using annotations from existing datasets, the model gains fundamental abilities essential for fine-grained image perception. A self-consistent bootstrapping method extends object annotations to referring-expression-bounding-box pairs, generating high-quality data. The visual encoder is tuned during instruction tuning to enhance fine-grained image perception. Experimental results show superior performance over existing models on various tasks.

סטטיסטיקה

Pink exhibits a 5.2% accuracy improvement over Qwen-VL on GQA.
Pink surpasses the accuracy of Kosmos-2 by 24.7% on RefCOCO val.
Pink attains the top rank on the leaderboard of MM-Bench.

ציטוטים

"Our method leverages existing datasets to cover a wide variety of RC tasks."
"The proposed self-consistent bootstrapping method significantly reduces the cost of generating high-quality datasets."
"Our model achieves superior performance with fewer trainable parameters and less training data."

תובנות מפתח מזוקקות מ:

Pink

by Shiyu Xuan,Q... ב- arxiv.org 03-14-2024

https://arxiv.org/pdf/2310.00582.pdf

שאלות מעמיקות

How can the proposed framework be applied to other types of models beyond MLLMs?

The proposed framework for enhancing fine-grained image perception ability through RC tasks can be adapted and applied to various types of models beyond MLLMs. For instance, the method of constructing an instruction tuning dataset by converting annotations from existing datasets into diverse RC tasks can be utilized in training different types of vision-language models or even general machine learning models that require multi-modal comprehension. The self-consistent bootstrapping method, which extends object annotations to referring-expression-bounding-box pairs, can also be implemented in other model architectures that involve object detection or visual grounding tasks.

What are potential limitations or challenges in implementing the self-consistent bootstrapping method?

While the self-consistent bootstrapping method is effective in extending object annotations to referring-expression-bounding-box pairs, there are some potential limitations and challenges in its implementation:

Quality Control: Ensuring the quality of generated descriptions and filtering out low-quality data based on predefined thresholds may require manual intervention or additional validation steps.
Scalability: Scaling up this method to handle a large number of objects or complex scenes could increase computational requirements and processing time.
Generalization: The effectiveness of this method may vary depending on the diversity and complexity of objects present in different datasets, potentially limiting its generalizability across all scenarios.
Noise Handling: Dealing with noisy descriptions generated by the model during bootstrapping poses a challenge as it may impact downstream performance if not properly addressed.

How might leveraging diverse RC tasks impact the generalization ability of MLLMs?

Leveraging diverse Referential Comprehension (RC) tasks during instruction tuning has several implications for improving the generalization ability of Multi-modal Large Language Models (MLLMs):

Enhanced Adaptability: By exposing MLLMs to a wide range of RC tasks related to fundamental abilities like visual relation reasoning and spatial reasoning, they become more adaptable to varying contexts and scenarios.
Improved Robustness: Training on diverse RC tasks helps MLLMs develop robust understanding capabilities that enable them to generalize better across different domains and applications.
Increased Flexibility: Exposure to varied RC tasks allows MLLMs to learn multiple ways of interpreting instructions and handling complex visual inputs, leading to enhanced flexibility in their responses.
Better Transfer Learning: The skills acquired through diverse RC tasks make MLLMs more adept at transferring knowledge from one task/domain to another, thereby improving their overall generalization performance across a broad spectrum of applications.

By incorporating a range of challenging RC tasks during training, MLLMs can develop stronger foundational abilities that contribute significantly towards their capacity for generalized learning and improved performance on unseen data sets or real-world scenarios.