Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision

• URL: http://arxiv.org/abs/2501.13353v1
• Date: Thu, 23 Jan 2025 03:34:14 GMT
• Title: Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision
• Authors: Aman Urumbekov, Zheng Chen,
• Abstract summary: We propose textbfContrast, a hybrid SR model that combines textbfConvolutional, textbfTransformer, and textbfState Space components. By integrating transformer and state space mechanisms, textbfContrast compensates for the shortcomings of each approach, enhancing both global context modeling and pixel-level accuracy.
• Score: 3.574664325523221
• License:
• Abstract: Transformers have become increasingly popular for image super-resolution (SR) tasks due to their strong global context modeling capabilities. However, their quadratic computational complexity necessitates the use of window-based attention mechanisms, which restricts the receptive field and limits effective context expansion. Recently, the Mamba architecture has emerged as a promising alternative with linear computational complexity, allowing it to avoid window mechanisms and maintain a large receptive field. Nevertheless, Mamba faces challenges in handling long-context dependencies when high pixel-level precision is required, as in SR tasks. This is due to its hidden state mechanism, which can compress and store a substantial amount of context but only in an approximate manner, leading to inaccuracies that transformers do not suffer from. In this paper, we propose \textbf{Contrast}, a hybrid SR model that combines \textbf{Con}volutional, \textbf{Tra}nsformer, and \textbf{St}ate Space components, effectively blending the strengths of transformers and Mamba to address their individual limitations. By integrating transformer and state space mechanisms, \textbf{Contrast} compensates for the shortcomings of each approach, enhancing both global context modeling and pixel-level accuracy. We demonstrate that combining these two architectures allows us to mitigate the problems inherent in each, resulting in improved performance on image super-resolution tasks.

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