MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer

Temel Kavramlar

The author introduces the MADTP framework to accelerate Vision-Language Transformers by aligning features across modalities and dynamically adjusting token compression ratios based on input complexity.

Özet

The MADTP framework aims to reduce computational costs of VLTs through token pruning. It integrates the MAG module for alignment and the DTP module for dynamic pruning. Extensive experiments show significant performance improvements while reducing GFLOPs.
Existing VLT models face high computational costs due to complex architectures and numerous tokens. MADTP addresses this by aligning features across modalities and dynamically pruning tokens based on input complexity. The MAG module ensures alignment between visual and language tokens, while the DTP module adjusts compression ratios adaptively.
Through experiments on various datasets, MADTP demonstrates superior performance in tasks like Visual Reasoning, Image Captioning, Image-Text Retrieval, and Visual Question Answering. It significantly reduces computational complexity while maintaining competitive performance levels.
Key components of MADTP include Token Importance Scores (TIS), Multi-modality Alignment Guidance (MAG) module, and Dynamic Token Pruning (DTP) module. Hyperparameter tuning plays a crucial role in optimizing model compression results.

İstatistikler

MADTP can reduce GFLOPs by 80% with less than 4% performance degradation.
Upop suggests a unified parameter pruning strategy for compressing VLTs.
ELIP introduces a vision token pruning method to remove less influential tokens based on language outputs.
CrossGET implements token pruning by selectively eliminating redundant tokens.
Existing popular VLT models consist of multiple modality-specific sub-modules.
Different input samples often require different levels of computation complexity for inference.
Dynamic token pruning works focus on single-modality compression but lack consideration for multi-modalities.
MADTP integrates MAG module for alignment and DTP module for dynamic token pruning.

Alıntılar

"The proposed MADTP framework aims to reduce computational complexity while preserving competitive performance."
"MADTP significantly reduces the computational complexity of multimodal models."
"Our main contributions include revealing the vital role of aligning multi-modalities for guiding VLT compression."

Önemli Bilgiler Şuradan Elde Edildi

MADTP

by Jianjian Cao... : arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02991.pdf

Daha Derin Sorular

How does the introduction of cross-modality alignment improve token pruning effectiveness?

The introduction of cross-modality alignment in token pruning, as seen in the MADTP framework, significantly improves the effectiveness of the pruning process. By aligning features from different modalities using learnable tokens, MADTP ensures that tokens are pruned based on their relevance across all modalities rather than just within a single modality. This alignment helps to identify and eliminate tokens that are less important for all modalities, thus avoiding situations where crucial tokens for one modality are mistakenly pruned due to their perceived insignificance in another modality. Ultimately, this approach leads to more efficient compression of VLT models by ensuring that only redundant or less important tokens are removed while preserving essential information necessary for model performance.

How can MADTP be extended to address other challenges in multimodal learning beyond token compression?

MADTP can be extended to address various challenges in multimodal learning beyond token compression by incorporating additional modules or techniques tailored to specific needs. Some possible extensions include:

Modifying Loss Functions: Introducing new loss functions or regularization terms within MADTP to enhance model training and performance.

Incorporating Attention Mechanisms: Integrating attention mechanisms into MADTP to improve feature selection and focus on relevant parts of input data during processing.

Exploring Transfer Learning: Leveraging transfer learning techniques within MADTP to adapt knowledge learned from one task/domain to another related task/domain efficiently.

Enhancing Model Interpretability: Developing methods within MADTP that provide insights into how decisions are made by the model, improving transparency and interpretability.

By expanding its capabilities beyond token compression alone, MADTP can become a versatile framework capable of addressing a wide range of challenges encountered in multimodal learning tasks.

What are the implications of dynamic token pruning across different layers of VLT models?

Dynamic token pruning across different layers of VLT models has several implications:

Adaptive Compression: Dynamic token pruning allows for adaptive adjustment of the model's compression ratio based on varying complexities present at different layers and input instances. This adaptability ensures optimal resource allocation throughout the network.

Efficient Resource Utilization: By dynamically adjusting the number of retained tokens at each layer based on importance scores calculated considering both intra- and inter-modal relationships, resources such as memory usage and computational power can be utilized more efficiently.

Improved Performance-Complexity Trade-off: Dynamic token pruning enables fine-tuning the balance between model performance and computational complexity at each layer individually, leading to an optimized trade-off between accuracy and efficiency.

4Flexibility Across Tasks: The ability to dynamically prune tokens based on varying requirements per layer makes VLT models more flexible when applied across diverse tasks with differing levels of complexity or data characteristics.
Overall, dynamic token pruning enhances flexibility, efficiency, and performance optimization in VLT models by tailoring resource allocation accordingto specific needs at different layers during inference processes..