Following on the epoch-making performance of transformer architecture-based large language models, vision transformers (ViTs) have emerged as a powerful approach to image processing. Like their text-based predecessors, ViTs initially relied on multi-headed self-attention layers to capture features from input images, while more recent approaches have employed spectral layers to represent image patches in the frequency domain. Could ViTs benefit from an architecture that incorporates both methods?
In the new paper SpectFormer: Frequency and Attention Is What You Need in a Vision Transformer, a research team from Microsoft and the University of Bath proposes SpectFormer, a novel transformer architecture that combines spectral and multi-headed attention layers to better capture appropriate feature representations and improve ViT performance.
The team summarizes their main contributions as follows:
- We design SpectFormer by using initial spectral layers and multi-headed attention in deeper layers. We validate the choice of this architecture through thorough empirical validation.
- We show that SpectFormer gets reasonable performance when used in transfer learning mode (trained on ImageNet and tested on CIFAR datasets) on CIFAR-10, and CIFAR-100 datasets.
- Further, we show that SpectFormer obtains consistent performance in other tasks such as object detection and instance segmentation by evaluating its performance on the MS COCO dataset.
The team first explores how different combinations of spectral and multi-headed attention layers perform compared to exclusively attention or spectral models, concluding that equipping their proposed SpectFormer with initial spectral layers implemented with Fourier Transform followed by multi-headed attention layers achieves the most promising results.
The SpectFormer architecture has four main components: a patch embedding layer, a positional embedding layer, a transformer block comprising a series of spectral layers followed by attention layers, and a classification head. The SpectFormer pipeline first transforms image tokens to the Fourier domain (into spectral space), where a frequency-based analysis of the image information is performed and relevant features captured; then applies gating techniques via learnable weight parameters; and finally performs an inverse Fourier transform to return the signal from the spectral space to the physical space.
In their empirical study, the team compared SpectFormer with the multi-headed self-attention-based DeIT, the parallel architecture LiT, and the spectral-based GFNet ViTs on various object detection and image classification tasks. SpectFormer bettered all baselines in the experiments, achieving state-of-the-art top-1 accuracy (85.7%) on the ImageNet-1K dataset.
Author: Hecate He | Editor: Michael Sarazen
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