SpectFormer is a novel transformer architecture for image processing using a combination of multi-headed self-attention and spectral layers proposed by researchers at Microsoft. This paper highlights how SpectFormer’s proposed architecture better captures good feature representations and improves the performance of Vision Transformer (ViT).
The research team first looked at how different combinations of spectral models and multi-headed attention layers compare to models using attention models or spectral models alone. This group came to the conclusion that the most promising results were obtained with his SpectFormer’s proposed design. This included a spectral layer, first implemented using Fourier transforms, followed by a multi-headed attentional layer.
The SpectFormer architecture consists of four basic parts. A classification head, a transform block consisting of a series of spectral layers followed by an attention layer, and a patch embedding layer. The pipeline performs a frequency-based analysis of the image information and transforms the image tokens into the Fourier domain using the Fourier transform to capture important features. The signal is then returned from spectral space to physical space using an inverse Fourier transform, learnable weight parameters, and a gating algorithm.
The team validated SpectFormer’s architecture using empirical validation and showed that it works very well in transfer learning mode on the CIFAR-10 and CIFAR-100 datasets. Scientists have also demonstrated that object detection and instance segmentation tasks evaluated on the MS COCO dataset yield consistent results using SpectFormer.
The researchers compared SpectFormer to multi-headed self-attention-based DeIT, parallel architecture LiT, and spectral-based GFNet ViT for various object identification and image classification tasks. In the study, SpectFormer exceeded all baselines and achieved top 1 accuracy on the ImageNet-1K dataset, exceeding current standards by 85.7%.
The results show that the proposed design of SpectFormer, which combines a spectral layer and a multi-headed attention layer, may capture good feature representations more effectively and improve ViT performance. The SpectFormer results offer hope for further research on vision transformers that combine both techniques.
The team has made two contributions in this area. The first proposes SpectFormer, a novel design that blends a spectral layer with a multi-headed attention layer to increase image processing efficiency. He then validates his SpectFormer on multiple object detection and image classification tasks, demonstrating its effectiveness by obtaining top-1 accuracy on the ImageNet-1K dataset, which is state-of-the-art in this field.
All things considered, SpectFormer provides a viable avenue for future research on vision transformers combining spectral and multi-headed attention layers. SpectFormer’s proposed design may play an important role in image processing pipelines with more investigation and validation.
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Niharika is a technical consulting intern at Marktechpost. She is in her third year of undergraduate studies and is currently completing her Bachelor’s degree at the Indian Institute of Technology (IIT), Kharagpur. She is a very passionate person who has a keen interest in machine learning, data her science, AI and avid reader of the latest developments in these fields.
