Analyzing Multimodal In-Context Learning for Vision & Language Models

Temel Kavramlar

Improving Vision & Language Models through ICL instruction tuning.

Özet

This content delves into the analysis of Multimodal In-Context Learning (ICL) for Vision & Language Models. It discusses the importance of ICL in enhancing model performance and proposes strategies to improve it. The study includes comparisons with strong baselines and evaluations on various tasks, such as fine-grained few-shot visual recognition and ICL benchmarks.
Structure:

Introduction to Large Language Models (LLMs)
Fusion of LLMs with other modalities like vision
Challenges faced by leading Visual and Language Models (VLMs) in ICL
Proposed strategy for improving ICL performance in VLMs
Evaluation of proposed approach on various tasks and datasets
Ablation studies on data mixing strategies, instruction formats, and shared semantic concepts within ICL instructions
Scaling potential of the proposed approach
Preservation of base model capabilities during ICL tuning
Leveraging shot information for improved model performance

İstatistikler

Inspired by the emergence of Large Language Models (LLMs)
Vision modality resulting in powerful zero-shot performance.
21.03% (and 11.3% on average) ICL performance boost.
Significant improvements over strong baselines.
Improved by over 12% on fine-grained few-shot visual recognition tasks.

Alıntılar

"In this work, we dive deeper into analyzing the capabilities of some of the state-of-the-art VLMs to follow ICL instructions."
"Equipped with this adaptation of the visual instruction tuning, we explore and provide insights on effective data mixes."

Önemli Bilgiler Şuradan Elde Edildi

Towards Multimodal In-Context Learning for Vision & Language Models

by Sivan Doveh,... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12736.pdf

Daha Derin Sorular

How can incorporating semantically-coherent ICL tasks enhance VLM performance?

Incorporating semantically-coherent In-Context Learning (ICL) tasks can significantly enhance Vision and Language Model (VLM) performance by providing a structured and focused training approach. By designing ICL instructions with shared semantic concepts across all shots, the model learns to leverage this information effectively during inference. This helps the model understand the relationships between different elements in a task, leading to more accurate predictions and improved generalization capabilities. Additionally, having coherent ICL tasks ensures that the model receives consistent and relevant information, which is crucial for fine-tuning its understanding of complex visual-language interactions.

How does preserving base model capabilities impact overall model performance?

Preserving base model capabilities is essential for maintaining the fundamental strengths of the aligned VLM when introducing new training paradigms like ICL instruction tuning. By replaying non-ICL data from the base model during training, we ensure that the core abilities of the VLM are retained while enhancing its few-shot learning capabilities through ICL instructions. This preservation not only prevents forgetting important knowledge encoded in the base model but also provides a solid foundation for building on top of existing skills. Ultimately, maintaining these base capabilities contributes to overall model robustness and adaptability across various tasks.

What are the implications of scaling up ICL instruction data on model improvement?

Scaling up In-Context Learning (ICL) instruction data has several implications on improving VLM performance:

Enhanced Generalization: Increasing the amount of diverse ICL instruction data allows models to learn from a wider range of examples, leading to better generalization across different tasks.
Improved Adaptability: With more varied training instances, models become more adaptable to novel scenarios and can handle unseen challenges with greater ease.
Increased Accuracy: Larger datasets provide models with richer context and information, resulting in higher accuracy levels in performing complex vision-language tasks.
Potential for Specialization: Scaling up ICL instruction data opens up opportunities for specialized training on specific domains or applications within vision-language modeling.
Overall, scaling up ICL instruction data plays a crucial role in refining models' abilities by exposing them to diverse patterns and contexts present in real-world scenarios.