Vision: NesT with JAX/Flax - Nested Hierarchical Transformer

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Nested Hierarchical Transformer

NesT is a simple method, which aggregates nested local transformers on image blocks. The idea makes vision transformers attain better accuracy, data efficiency, and convergence on the ImageNet benchmark. NesT can be scaled to small datasets to match convnet accuracy.

Blog post: https://ai.googleblog.com/2022/02/nested-hierarchical-transformer-towards.html
GitHub: https://github.com/google-research/nested-transformer
arXiv: https://arxiv.org/pdf/2105.12723.pdf

In visual understanding, the Visual Transformer (ViT) and its variants have received significant attention recently due to their superior performance on many core visual applications, such as image classification, object detection, and video understanding. The core idea of ViT is to utilize the power of self-attention layers to learn global relationships between small patches of images. However, the number of connections between patches increases quadratically with image size. Such a design has been observed to be data inefficient — although the original ViT can perform better than convolutional networks with hundreds of millions of images for pre-training, such a data requirement is not always practical, and it still underperforms compared to convolutional networks when given less data. Many are exploring to find more suitable architectural re-designs that can learn visual representations effectively, such as by adding convolutional layers and building hierarchical structures with local self-attention.

The principle of hierarchical structure is one of the core ideas in vision models, where bottom layers learn more local object structures on the high-dimensional pixel space and top layers learn more abstracted and high-level knowledge at low-dimensional feature space. Existing ViT-based methods focus on designing a variety of modifications inside self-attention layers to achieve such a hierarchy, but while these offer promising performance improvements, they often require substantial architectural re-designs. Moreover, these approaches lack an interpretable design, so it is difficult to explain the inner-workings of trained models.

To address these challenges, in “Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding”, we present a rethinking of existing hierarchical structure–driven designs, and provide a novel and orthogonal approach to significantly simplify them. The central idea of this work is to decouple feature learning and feature abstraction (pooling) components: nested transformer layers encode visual knowledge of image patches separately, and then the processed information is aggregated. This process is repeated in a hierarchical manner, resulting in a pyramid network structure. The resulting architecture achieves competitive results on ImageNet and outperforms results on data-efficient benchmarks. We have shown such a design can meaningfully improve data efficiency with faster convergence and provide valuable interpretability benefits. Moreover, we introduce GradCAT, a new technique for interpreting the decision process of a trained model at inference time.