ADEM-VL: Adaptieve en Ingebouwde Fusie voor Efficiënte Visie-Taal Afstelling

ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning

October 23, 2024
Auteurs: Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo, Han Hu, Yonggang Wen
cs.AI

Samenvatting

Recente ontwikkelingen in multimodale fusie hebben de opmerkelijke successen gezien van visie-taal (VT) modellen, die uitblinken in verschillende multimodale toepassingen zoals beeldbeschrijving en visuele vraagbeantwoording. Echter, het bouwen van VT modellen vereist aanzienlijke hardwarebronnen, waar efficiëntie beperkt wordt door twee belangrijke factoren: de uitgebreide invoerreeks van het taalmodel met visuele kenmerken vereist meer rekenkundige bewerkingen, en een groot aantal extra leerparameters verhoogt de geheugencomplexiteit. Deze uitdagingen beperken aanzienlijk de bredere toepasbaarheid van dergelijke modellen. Om deze kloof te overbruggen, stellen wij ADEM-VL voor, een efficiënte visie-taal methode die VT modellen afstemt op vooraf getrainde grote taalmodellen (TTM's) door het aannemen van een parameterloos kruis-aandachtsmechanisme voor gelijkenismetingen in multimodale fusie. Deze aanpak vereist enkel het inbedden van visuele kenmerken in de taalruimte, waardoor het aantal trainbare parameters aanzienlijk wordt verminderd en zowel de trainingssnelheid als inferentiesnelheden worden versneld. Om de representatie-leren in het fusiemodule te verbeteren, introduceren we een efficiënt multischaal kenmerkengeneratieschema dat slechts een enkele voorwaartse doorgang door de visie-encoder vereist. Bovendien stellen we een adaptief fusieschema voor dat dynamisch minder relevante visuele informatie voor elk teksttoken verwerpt op basis van zijn aandachtscore. Dit zorgt ervoor dat het fusieproces de meest pertinente visuele kenmerken prioriteert. Met experimenten op verschillende taken, waaronder visuele vraagbeantwoording, beeldbeschrijving en instructievolging, tonen we aan dat ons raamwerk bestaande benaderingen overtreft. Specifiek overtreft onze methode bestaande methoden met een gemiddelde nauwkeurigheid van 0,77% op de ScienceQA dataset, met verminderde training en inferentievertraging, waarbij de superioriteit van ons raamwerk wordt aangetoond. De code is beschikbaar op https://github.com/Hao840/ADEM-VL.
English
Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioning and visual question answering. However, building VL models requires substantial hardware resources, where efficiency is restricted by two key factors: the extended input sequence of the language model with vision features demands more computational operations, and a large number of additional learnable parameters increase memory complexity. These challenges significantly restrict the broader applicability of such models. To bridge this gap, we propose ADEM-VL, an efficient vision-language method that tunes VL models based on pretrained large language models (LLMs) by adopting a parameter-free cross-attention mechanism for similarity measurements in multimodal fusion. This approach only requires embedding vision features into the language space, significantly reducing the number of trainable parameters and accelerating both training and inference speeds. To enhance representation learning in fusion module, we introduce an efficient multiscale feature generation scheme that requires only a single forward pass through the vision encoder. Moreover, we propose an adaptive fusion scheme that dynamically discards less relevant visual information for each text token based on its attention score. This ensures that the fusion process prioritizes the most pertinent visual features. With experiments on various tasks including visual question answering, image captioning, and instruction-following, we demonstrate that our framework outperforms existing approaches. Specifically, our method surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset, with reduced training and inference latency, demonstrating the superiority of our framework. The code is available at https://github.com/Hao840/ADEM-VL.

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.

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