ADEM-VL: Adaptieve en Ingebouwde Fusie voor Efficiënte Visie-Taal Afstelling
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
Samenvatting
Summary
AI-Generated Summary
Paper Overview
Recent advancements in multimodal fusion have led to the success of vision-language (VL) models in applications like image captioning and visual question answering. ADEM-VL is proposed as an efficient vision-language method that embeds vision features into the language space, reducing trainable parameters and achieving superior performance while maintaining high efficiency compared to existing methods.
Core Contribution
ADEM-VL introduces an efficient vision-language method that tunes VL models based on pretrained large language models (LLMs) using a parameter-free cross-attention mechanism. It significantly reduces the number of trainable parameters, accelerates training and inference speeds, and outperforms existing methods in tasks like visual question answering and image captioning.
Research Context
The research focuses on developing methods for efficient multimodal fusion in VL models. ADEM-VL is designed to simplify cross-attention modules, reduce computational requirements, introduce parameter and computational efficiency, and enhance performance in vision-language tasks.
Keywords
Multimodal Fusion, Vision-Language Models, ADEM-VL, Cross-Attention Mechanism, Large Language Models, Efficient Parameterization
Background
The research addresses challenges in building VL models due to extended input sequences and increased memory complexity. ADEM-VL simplifies the standard cross-attention module, introduces efficient multiscale feature generation, and proposes an adaptive fusion scheme to enhance the model's focus on relevant visual information.
Research Gap
Existing literature lacks efficient methods for multimodal fusion in VL models that reduce trainable parameters and computational costs while maintaining high performance.
Technical Challenges
Building VL models faces obstacles related to extended input sequences, increased memory complexity, and the need for efficient multimodal fusion techniques.
Prior Approaches
Previous solutions have not effectively addressed the challenges of parameter efficiency and computational costs in multimodal fusion for VL models.
Methodology
ADEM-VL's methodology is based on a parameter-free cross-attention mechanism, multiscale visual feature generation, and an adaptive fusion scheme to enhance the model's focus on informative visual information.
Theoretical Foundation
The methodology is grounded in mathematical principles of cross-attention mechanisms, kernel tricks, ReLU-like activation functions, and parameter-efficient projections.
Technical Architecture
ADEM-VL's system design includes a vision tower with lightweight adapters, learnable positional embeddings, and efficient cross-attention modules integrated into large language models.
Implementation Details
Specific algorithms such as identity matrices for projection, pooling operations for multiscale visual features, and adaptive fusion schemes are crucial components of ADEM-VL.
Innovation Points
ADEM-VL innovates by reducing trainable parameters, introducing efficient cross-attention mechanisms, and optimizing the model's focus on relevant visual information.
Experimental Validation
Experimental validation demonstrates ADEM-VL's superior performance in tasks like visual question answering, image captioning, and instruction-following, with reduced training and inference latency.
Setup
Exact configurations include parameter-free cross-attention, multiscale visual feature generation, and adaptive fusion schemes, evaluated on datasets like ScienceQA and COCO Caption.
Metrics
Evaluation criteria involve accuracy improvements, reduced training and inference latency, and comparison with existing methods in vision-language tasks.
Results
Quantitative findings show ADEM-VL achieves a 0.77% higher accuracy on the ScienceQA dataset, demonstrating improved performance and efficiency.
Comparative Analysis
Comparisons with existing methods highlight ADEM-VL's superiority in terms of parameters, computational costs, and performance in vision-language tasks.
Impact and Implications
ADEM-VL's impact lies in its efficient multimodal fusion approach, superior performance, and reduced computational costs, with implications for future research and practical applications.
Key Findings
The key contributions include improved performance in VL tasks, reduced trainable parameters, and enhanced efficiency in training and inference.
Limitations
Limitations may include specific task dependencies, further optimization opportunities, and potential challenges in scaling the framework to larger datasets.
Future Directions
Future research opportunities involve enhancing the adaptive fusion module, exploring differences between VL models and human perception, and optimizing performance in various vision-language tasks.
Practical Significance
The practical applications of ADEM-VL include efficient image captioning, visual question answering systems, and instruction-following models, with implications for real-world multimodal tasks.