Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection.

Reza Azad, Amirhossein Kazerouni, Babak Azad, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof
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Abstract

Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87% and +0.76% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub.

拉普拉斯变换器克服视觉变换器在局部纹理检测中的局限性
视觉变换器(ViT)模型在广泛的计算机视觉任务中取得了突破性进展。然而,与卷积神经网络(CNN)模型相比,人们发现 ViT 模型很难捕捉到图像的高频成分,从而限制了其检测局部纹理和边缘信息的能力。由于肿瘤和病变等人体组织异常在结构、纹理和形状上可能存在很大差异,因此纹理等高频信息对于有效的语义分割任务至关重要。为了解决 ViT 模型中的这一局限性,我们提出了一种新技术--拉普拉斯矩阵(Laplacian-Former),该技术通过自适应地重新校准拉普拉斯金字塔中的频率信息来增强自我关注图。更具体地说,我们提出的方法通过高效注意力和频率注意力利用了双重注意力机制,而高效注意力机制在产生相同输出的同时将自我注意力的复杂性降低为线性,选择性地强化了形状和纹理特征的贡献。此外,我们还引入了一种新颖的高效增强多尺度桥,可有效地将空间信息从编码器传输到解码器,同时保留基本特征。我们证明了拉普拉斯公式在多器官和皮肤病变分割任务中的功效,与 SOTA 方法相比,骰子得分分别提高了 +1.87% 和 +0.76%。我们的实现可在 GitHub 上公开获取。
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