A Close Look at Spatial Modeling: From Attention to Convolution (2212.12552v1)

Abstract: Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism. By revisiting the self-attention responses in Transformers, we empirically observe two interesting issues. First, Vision Transformers present a queryirrelevant behavior at deep layers, where the attention maps exhibit nearly consistent contexts in global scope, regardless of the query patch position (also head-irrelevant). Second, the attention maps are intrinsically sparse, few tokens dominate the attention weights; introducing the knowledge from ConvNets would largely smooth the attention and enhance the performance. Motivated by above observations, we generalize self-attention formulation to abstract a queryirrelevant global context directly and further integrate the global context into convolutions. The resulting model, a Fully Convolutional Vision Transformer (i.e., FCViT), purely consists of convolutional layers and firmly inherits the merits of both attention mechanism and convolutions, including dynamic property, weight sharing, and short- and long-range feature modeling, etc. Experimental results demonstrate the effectiveness of FCViT. With less than 14M parameters, our FCViT-S12 outperforms related work ResT-Lite by 3.7% top1 accuracy on ImageNet-1K. When scaling FCViT to larger models, we still perform better than previous state-of-the-art ConvNeXt with even fewer parameters. FCViT-based models also demonstrate promising transferability to downstream tasks, like object detection, instance segmentation, and semantic segmentation. Codes and models are made available at: https://github.com/ma-xu/FCViT.

• The paper reveals that deeper attention maps in Vision Transformers become query-irrelevant and sparse, motivating the shift to convolutional strategies.
• It introduces FCViT, which integrates global context into convolutions and achieves a 3.7% top-1 accuracy boost over ResT-Lite on ImageNet-1K.
• FCViT demonstrates versatility across tasks like object detection and segmentation while offering efficient performance with fewer parameters.

An Analytical Overview of "A Close Look at Spatial Modeling: From Attention to Convolution"

The research paper entitled "A Close Look at Spatial Modeling: From Attention to Convolution" explores the intricate dynamics of spatial modeling in Vision Transformers (ViTs) and proposes an innovative model, Fully Convolutional Vision Transformer (FCViT), which merges the strengths of both Transformers and Convolutional Networks (ConvNets). This paper navigates through two specific phenomena observed in ViTs: query-irrelevant behavior in attention maps at deeper layers, and the intrinsic sparsity of these maps. The researchers argue that the learning benefits from both architectures can be harnessed by constructing a model composed entirely of convolutional layers.

Key Observations and Model Development

The paper commences by investigating self-attention in Vision Transformers, highlighting two pivotal observations based on empirical analysis. Firstly, the attention maps tend to become query-irrelevant in deeper layers, exhibiting homogeneity across various query positions. This contradicts the expected behavior of multi-head self-attention, where each attention map should be unique and dependent on the query token. Secondly, attention maps are found to be sparse, with a minimal number of dominating tokens. The incorporation of convolutional insights can significantly ameliorate these maps, resulting in smoother distributions and enhanced performance metrics.

Motivated by these observations, the authors generalize the self-attention function to extract a query-independent global context. This global context is then dynamically integrated into convolutions, leading to the design of the Fully Convolutional Vision Transformer. FCViT preserves advantageous characteristics like dynamic property, weight sharing, and the ability to capture both short and long-range dependencies. The proposed architecture consists purely of convolutional layers, yet it effectively embodies the merits traditionally attributed to the attention mechanisms in Transformers.

Empirical Validation and Implications

Experimentation substantiates the efficacy of the FCViT model; the FCViT-S12 variant, with less than 14 million parameters, surpasses ResT-Lite by a margin of 3.7% in top-1 accuracy on the ImageNet-1K dataset. This demonstrates that FCViT not only maintains but often exceeds the performance of existing state-of-the-art models, and it does so with fewer parameters and computational resources. Importantly, FCViT's robustness extends beyond classification tasks, showing significant promise in object detection, instance segmentation, and semantic segmentation when evaluated across diverse downstream tasks.

Theoretical and Practical Implications

From a theoretical standpoint, this paper challenges the established paradigms surrounding the necessity of the attention mechanism in Vision Transformers. By demonstrating that a convolutional architecture can effectively emulate the critical functions of attention, this work invites a reevaluation of spatial relationship modeling within neural networks.

Practically, FCViT opens pathways for more resource-efficient deployments of neural networks in real-world applications. The reduced parameter footprint and computational demand make FCViT a lucrative candidate for environments where computational resources are limited. Furthermore, its impressive transferability to different visual tasks underscores its versatility.

Future Developments

The implications of this work suggest numerous avenues for future research. Possible directions include further refinement of the FCViT architecture to push its performance limits, exploration of hybrid models integrating additional architectural innovations, and investigations into the scalability of the proposed method for even larger datasets and more complex vision tasks.

In summary, this paper presents a compelling analysis and development of convolutional methods for spatial modeling, challenging the current doctrines of Vision Transformers while laying a foundation for future explorations into the unification of diverse neural network architectures.