On Efficient Transformer-Based Image Pre-training for Low-Level Vision (2112.10175v2)

Abstract: Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing systems. In this paper, we tailor transformer-based pre-training regimes that boost various low-level tasks. To comprehensively diagnose the influence of pre-training, we design a whole set of principled evaluation tools that uncover its effects on internal representations. The observations demonstrate that pre-training plays strikingly different roles in low-level tasks. For example, pre-training introduces more local information to higher layers in super-resolution (SR), yielding significant performance gains, while pre-training hardly affects internal feature representations in denoising, resulting in limited gains. Further, we explore different methods of pre-training, revealing that multi-related-task pre-training is more effective and data-efficient than other alternatives. Finally, we extend our study to varying data scales and model sizes, as well as comparisons between transformers and CNNs-based architectures. Based on the study, we successfully develop state-of-the-art models for multiple low-level tasks. Code is released at https://github.com/fenglinglwb/EDT.

• The paper introduces a novel transformer pre-training strategy that leverages multi-related-task learning to boost low-level vision performance.
• It employs CKA analysis to reveal task-specific impacts, notably improving detail capture in super-resolution.
• The proposed Encoder-Decoder Transformer outperforms standard models on benchmarks like Urban100 and Manga109 while ensuring computational efficiency.

Efficient Transformer-Based Image Pre-training for Low-Level Vision

The presented paper offers an in-depth examination of transformer-based pre-training approaches tailored for low-level vision tasks. Specifically, it explores how pre-training regimes can be optimized for tasks like super-resolution (SR), denoising, and deraining. These tasks typically have limited data availability, which motivates the investigation into effective pre-training strategies to leverage large-scale datasets effectively.

Overview

In high-level computer vision tasks, pre-training has proven to be beneficial, particularly when data is scarce. However, its application to low-level vision tasks has been less explored. This research contributes by filling this gap, employing transformers, which have shown efficacy both in NLP and high-level vision tasks. The paper emphasizes the differences in pre-training effects across various low-level vision tasks and introduces a new transformer architecture, the Encoder-Decoder-based Transformer (EDT), which is highlighted for its computational efficiency.

Methodology and Key Findings

The paper employs several strategies to understand and evaluate the effects of image pre-training in low-level vision:

1. Internal Representation Analysis: The paper utilizes Centered Kernel Alignment (CKA) to analyze the internal representations of the models, discovering task-specific behaviors. For instance, pre-training prominently influences higher layers in SR, enhancing local detail capture, whereas its impact is more subdued in denoising tasks.
2. Pre-training Strategies: The research contrasts different pre-training strategies, finding that multi-related-task pre-training provides enhanced performance improvements over single-task or multi-unrelated-task pre-training methods. This is significant because it points towards the benefits of task correlation rather than sheer data volume.
3. Model Variability: The paper evaluates the influence of parameters like data scale and model size on the effectiveness of pre-training, concluding that larger models tend to benefit more from pre-training. Notably, the proposed EDT architecture is found to outperform standard architectures while maintaining computational efficiency.

The authors also release a set of pre-trained models that achieve state-of-the-art performance in SR, denoising, and deraining tasks. Notably, in the SR setting, their model significantly outperforms competitor models on popular benchmark datasets such as Urban100 and Manga109.

Implications and Future Directions

The implications of this research are threefold:

1. Practical Effectiveness: It provides clear evidence that pre-training can benefit low-level vision tasks, particularly when tasks are related, which can substantially enhance model performance without incurring a prohibitive computational cost.
2. Theoretical Understanding: By presenting a detailed analysis of how pre-training affects internal model representations, the paper contributes to a more nuanced understanding of inductive biases introduced by pre-training, particularly within transformer architectures.
3. Design Guidelines: The findings offer actionable insights and guidelines for designing efficient pre-training regimes, emphasizing the balance between model complexity, data utilization, and network architecture.

While the paper addresses gaps concerning low-level vision tasks, future research could explore real-world applications outside of synthesized data environments and expand into video processing or more advanced real-world scenarios. Furthermore, adaptive strategies that optimize model structure based on specific low-level image characteristics may present fruitful avenues for subsequent investigation.