MADTP++: Bridge the Gap Between Token and Weight Pruning for Accelerating VLTs
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View abstract on PubMed
Summary
This summary is machine-generated.MADTP++ offers a unified framework for compressing Vision-Language Transformers (VLTs) by simultaneously pruning tokens and weights. This approach significantly reduces computational costs and model parameters while maintaining performance.
Area Of Science
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
Background
- Vision-Language Transformers (VLTs) show great potential but suffer from high computational costs.
- Current compression methods for VLTs are limited, often ignoring cross-modal alignment and dynamic compression needs.
Purpose Of The Study
- To develop a novel, unified framework for efficient compression of Vision-Language Transformers.
- To address the limitations of existing methods in handling token and weight pruning simultaneously.
Main Methods
- Proposed MADTP++, integrating Multi-modality Alignment Guidance (MAG) and Dynamic Token Pruning (DTP) for token compression.
- Introduced Hardware-aware Weight Pruning (HWP) utilizing Sparse Tensor Cores for fine-grained weight pruning.
- Implemented a Cooperative Optimization Training Strategy with Knowledge Distillation Constraints for joint optimization.
Main Results
- MADTP++ significantly reduces model parameters and computational costs (GFLOPs).
- The method achieves superior compression compared to existing VLT compression techniques.
- Experiments show competitive performance is maintained across various VLT models and datasets.
Conclusions
- MADTP++ provides an effective and unified approach for compressing Vision-Language Transformers.
- The framework enables significant efficiency gains without compromising model performance.
- The proposed method offers a flexible and hardware-aware solution for VLT model optimization.
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