Vision Transformer Adapter for Dense Predictions (2205.08534v4)

Abstract: This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address this issue, we propose the ViT-Adapter, which allows plain ViT to achieve comparable performance to vision-specific transformers. Specifically, the backbone in our framework is a plain ViT that can learn powerful representations from large-scale multi-modal data. When transferring to downstream tasks, a pre-training-free adapter is used to introduce the image-related inductive biases into the model, making it suitable for these tasks. We verify ViT-Adapter on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation. Notably, without using extra detection data, our ViT-Adapter-L yields state-of-the-art 60.9 box AP and 53.0 mask AP on COCO test-dev. We hope that the ViT-Adapter could serve as an alternative for vision-specific transformers and facilitate future research. The code and models will be released at https://github.com/czczup/ViT-Adapter.

Vision Transformer Adapter for Dense Predictions: An Expert Overview

The paper presents a novel approach termed the Vision Transformer Adapter (ViT-Adapter), designed to enhance the performance of plain Vision Transformers (ViT) in dense prediction tasks such as object detection and semantic segmentation. Unlike traditional methods that rely on vision-specific architectures with embedded inductive biases, the ViT-Adapter integrates image-specific features through a pre-training-free adapter module, allowing the plain ViT to match or surpass the capabilities of specialized transformer models.

Core Contributions

1. ViT-Adapter Architecture: The proposed solution effectively injects vision-specific inductive biases into the ViT by incorporating three key modules: a spatial prior module, a spatial feature injector, and a multi-scale feature extractor. These components work collectively to adapt the plain ViT for dense prediction tasks without altering its structural integrity.
2. Performance on Dense Prediction Benchmarks: The paper details robust performance of the ViT-Adapter across multiple datasets and tasks. Notably, the ViT-Adapter-L configuration achieved a box AP of 60.9 and a mask AP of 53.0 on the COCO test-dev set, showcasing its competitiveness alongside state-of-the-art models.
3. Flexibility with Advanced Pre-training: The ViT-Adapter's design allows for efficient integration with multi-modal pre-training methodologies. This adaptability underscores its potential for broader applications beyond conventional image pre-training approaches.

Numerical and Experimental Insights

Empirical results demonstrate significant performance gains across various model sizes and configurations. For example, using the Mask R-CNN framework, ViT-Adapter-S improves on the plain ViT, achieving a 48.2 AP with a parameter increase from 43.8M to 47.8M. Such improvements underline the efficacy of the proposed adapter in enhancing feature granularity and prediction accuracy.

Implications and Future Directions

The ViT-Adapter paves the way for a flexible and scalable approach to leveraging Vision Transformers in complex vision tasks. Its ability to effortlessly integrate with advanced pre-training modalities suggests a promising direction for future research, particularly in enhancing model generalizability and representation learning. Additionally, the adapter-based strategy proposes an avenue for potentially reducing computational overhead in dense prediction tasks while maintaining high performance levels.

Conclusion

This work presents an innovative solution to bridge the gap between plain ViTs and vision-specific transformers in dense prediction tasks. The ViT-Adapter demonstrates substantial performance improvements and opens new pathways for research in vision transformers, emphasizing adaptability and efficiency in model training and deployment. As such, it stands as a valuable contribution to the field, offering practical insights for furthering AI advancements in computer vision.