Co-Scale Conv-Attentional Image Transformers (2104.06399v2)

Abstract: In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT's backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks.

• The paper introduces a co-scale mechanism that uses serial, parallel, and cross-scale attention to enhance multi-scale image modeling.
• It employs a conv-attentional module with convolution-like position embeddings to reduce the computational complexity of traditional self-attention.
• Experiments demonstrate state-of-the-art results on ImageNet and competitive performance on COCO for object detection and instance segmentation.

Analysis of Co-Scale Conv-Attentional Image Transformers

The paper introduces Co-scale conv-attentional image Transformers (CoaT), which is a novel approach in leveraging Transformers for image classification tasks. The primary innovation lies in the introduction of a co-scale mechanism and a conv-attentional mechanism that aims to enhance the performance of image classification models while maintaining computational efficiency. CoaT demonstrates effectiveness on the ImageNet dataset and shows applicability to downstream tasks such as object detection and instance segmentation.

Key Contributions

1. Co-Scale Mechanism: The authors propose a co-scale mechanism that involves maintaining encoder branches at separate scales and enabling attention across scales. This mechanism is realized through two building blocks: serial and parallel. These blocks facilitate fine-to-coarse, coarse-to-fine, and cross-scale image modeling, which enrich the multi-scale and contextual modeling capabilities of the Transformer architecture.
2. Conv-Attentional Module: A conv-attentional mechanism employing a convolution-like implementation to realize relative position embeddings is introduced. This efficient implementation strives to reduce the computational complexity of traditional self-attention mechanisms. It includes factorized attention and leverages convolutional operators for position encoding, improving both the efficiency and flexibility of Transformers in modeling spatial configurations.

Experimental Results

The paper presents state-of-the-art results on the ImageNet classification benchmark. CoaT models, in various sizes, outperform similar-sized convolutional neural networks (CNNs) and other Transformer-based image classifiers. For instance, the CoaT Tiny model achieves a Top-1 accuracy of 78.3\% on ImageNet with 5.5M parameters and 4.4 GFLOPs, outperforming the DeiT and PVT models with similar parameters.

Moreover, CoaT's backbone showcases its versatility in extending to object detection and instance segmentation tasks. In comparison with other backbones under the Mask R-CNN and Cascade Mask R-CNN frameworks, CoaT demonstrates competitive performance through improved average precision scores on the COCO dataset.

Theoretical and Practical Implications

The introduction of the co-scale mechanism signifies an advancement in multi-scale modeling within the Transformer architecture for vision tasks. This advancement offers theoretical insights into how different scale modes can be integrated effectively in Transformer models. Practically, the reduction in computational complexity achieved by the conv-attentional module makes CoaT more scalable for real-world applications, where computational resources can often be a limiting factor.

Speculations on Future Developments

Looking forward, advancements in AI models, particularly Transformer architectures, may continue to explore and refine multi-scale integration and position encoding strategies. Further research could investigate refining the co-scale mechanism, perhaps by enhancing interoperability and optimization across scales. Additionally, extending the conv-attentional mechanism to incorporate more diverse input types and larger spatial-temporal data sets could yield further enhancements in performance and utility.

In summary, the CoaT represents a substantial step in the development of efficient and effective Transformer-based models for computer vision tasks, setting a precedent for future work in this field.