Stronger ViTs With Octic Equivariance

• URL: http://arxiv.org/abs/2505.15441v2
• Date: Thu, 22 May 2025 15:33:46 GMT
• Title: Stronger ViTs With Octic Equivariance
• Authors: David Nordström, Johan Edstedt, Fredrik Kahl, Georg Bökman,
• Abstract summary: Vision Transformers (ViTs) incorporate weight sharing over image patches as an important inductive bias.<n>We develop new architectures, octic ViTs, that use octic-equivariant layers and put them to the test on both supervised and self-supervised learning.<n>We achieve approximately 40% reduction in FLOPs for ViT-H while simultaneously improving both classification and segmentation results.
• Score: 13.357266345180296
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent efforts at scaling computer vision models have established Vision Transformers (ViTs) as the leading architecture. ViTs incorporate weight sharing over image patches as an important inductive bias. In this work, we show that ViTs benefit from incorporating equivariance under the octic group, i.e., reflections and 90-degree rotations, as a further inductive bias. We develop new architectures, octic ViTs, that use octic-equivariant layers and put them to the test on both supervised and self-supervised learning. Through extensive experiments on DeiT-III and DINOv2 training on ImageNet-1K, we show that octic ViTs yield more computationally efficient networks while also improving performance. In particular, we achieve approximately 40% reduction in FLOPs for ViT-H while simultaneously improving both classification and segmentation results.

Related papers

• Block-Recurrent Dynamics in Vision Transformers [42.261020313952976]
  We argue that trained ViTs admit a block-recurrent depth structure such that the computation of the original $L$ blocks can be accurately rewritten using only $k ll L$ distinct blocks applied recurrently.<n>We train a Raptor model to recover $96%$ of DINOv2 ImageNet-1k linear probe accuracy in only 2 blocks at equivalent computational cost.
  arXiv  Detail & Related papers  (2025-12-23T00:18:23Z)
• Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials [34.77694214755808]
  We introduce Visual-Contrast Attention (VCA), a drop-in replacement for Multi-Head Self-Attention (MHSA)<n>VCA injects an explicit notion of discrimination while reducing the theoretical complexity from O(N N C) to O(N n C) with n N.<n>The module adds fewer than 0.3M parameters to a DeiT-Tiny backbone, requires no extra FLOPs, and is wholly architecture-agnostic.
  arXiv  Detail & Related papers  (2025-11-02T07:04:12Z)
• MOR-VIT: Efficient Vision Transformer with Mixture-of-Recursions [1.0411839100853515]
  MoR-ViT is a novel vision transformer framework that incorporates a token-level dynamic recursion mechanism.<n>Experiments on ImageNet-1K and transfer benchmarks demonstrate that MoR-ViT achieves state-of-the-art accuracy with up to 70% parameter reduction and 2.5x inference acceleration.
  arXiv  Detail & Related papers  (2025-07-29T12:46:36Z)
• Generalized Linear Mode Connectivity for Transformers [87.32299363530996]
  A striking phenomenon is linear mode connectivity (LMC), where independently trained models can be connected by low- or zero-loss paths.<n>Prior work has predominantly focused on neuron re-ordering through permutations, but such approaches are limited in scope.<n>We introduce a unified framework that captures four symmetry classes: permutations, semi-permutations, transformations, and general invertible maps.<n>This generalization enables, for the first time, the discovery of low- and zero-barrier linear paths between independently trained Vision Transformers and GPT-2 models.
  arXiv  Detail & Related papers  (2025-06-28T01:46:36Z)
• Transformers as Support Vector Machines [54.642793677472724]
  We establish a formal equivalence between the optimization geometry of self-attention and a hard-margin SVM problem. We characterize the implicit bias of 1-layer transformers optimized with gradient descent. We believe these findings inspire the interpretation of transformers as a hierarchy of SVMs that separates and selects optimal tokens.
  arXiv  Detail & Related papers  (2023-08-31T17:57:50Z)
• Experts Weights Averaging: A New General Training Scheme for Vision Transformers [57.62386892571636]
  We propose a training scheme for Vision Transformers (ViTs) that achieves performance improvement without increasing inference cost. During training, we replace some Feed-Forward Networks (FFNs) of the ViT with specially designed, more efficient MoEs. After training, we convert each MoE into an FFN by averaging the experts, transforming the model back into original ViT for inference.
  arXiv  Detail & Related papers  (2023-08-11T12:05:12Z)
• CageViT: Convolutional Activation Guided Efficient Vision Transformer [90.69578999760206]
  This paper presents an efficient vision Transformer, called CageViT, that is guided by convolutional activation to reduce computation. Our CageViT, unlike current Transformers, utilizes a new encoder to handle the rearranged tokens. Experimental results demonstrate that the proposed CageViT outperforms the most recent state-of-the-art backbones by a large margin in terms of efficiency.
  arXiv  Detail & Related papers  (2023-05-17T03:19:18Z)
• Rethinking Hierarchicies in Pre-trained Plain Vision Transformer [76.35955924137986]
  Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. customized algorithms should be carefully designed for the hierarchical ViTs, e.g., GreenMIM, instead of using the vanilla and simple MAE for the plain ViT. This paper proposes a novel idea of disentangling the hierarchical architecture design from the self-supervised pre-training.
  arXiv  Detail & Related papers  (2022-11-03T13:19:23Z)
• DeiT III: Revenge of the ViT [56.46810490275699]
  A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT.
  arXiv  Detail & Related papers  (2022-04-14T17:13:44Z)
• Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels [7.426118390008397]
  We evaluate Vision Transformers (ViT) training methods for image-based reinforcement learning control tasks. We compare these results to a leading convolutional-network architecture method, RAD. We find that the CNN architectures trained using RAD still generally provide superior performance.
  arXiv  Detail & Related papers  (2022-04-11T07:10:58Z)
• Improving Vision Transformers by Revisiting High-frequency Components [106.7140968644414]
  We show that Vision Transformer (ViT) models are less effective in capturing the high-frequency components of images than CNN models. To compensate, we propose HAT, which directly augments high-frequency components of images via adversarial training. We show that HAT can consistently boost the performance of various ViT models.
  arXiv  Detail & Related papers  (2022-04-03T05:16:51Z)
• TerViT: An Efficient Ternary Vision Transformer [21.348788407233265]
  Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. We introduce a ternary vision transformer (TerViT) to ternarize the weights in ViTs, which are challenged by the large loss surface gap between real-valued and ternary parameters.
  arXiv  Detail & Related papers  (2022-01-20T08:29:19Z)
• AdaViT: Adaptive Tokens for Efficient Vision Transformer [91.88404546243113]
  We introduce AdaViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. AdaViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds.
  arXiv  Detail & Related papers  (2021-12-14T18:56:07Z)
• Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training [29.20567759071523]
  Vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in computer vision. This paper introduces CNNs' inductive biases back to ViTs while preserving their network architectures for higher upper bound. Experiments on CIFAR-10/100 and ImageNet-1k with limited training data have shown encouraging results.
  arXiv  Detail & Related papers  (2021-12-07T07:56:50Z)
• Self-slimmed Vision Transformer [52.67243496139175]
  Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. We propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs.
  arXiv  Detail & Related papers  (2021-11-24T16:48:57Z)
• Global Vision Transformer Pruning with Hessian-Aware Saliency [93.33895899995224]
  This work challenges the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage. We derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter that utilizes parameters more efficiently.
  arXiv  Detail & Related papers  (2021-10-10T18:04:59Z)
• ViTGAN: Training GANs with Vision Transformers [46.769407314698434]
  Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. We introduce several novel regularization techniques for training GANs with ViTs. Our approach, named ViTGAN, achieves comparable performance to the leading CNN-based GAN models on three datasets.
  arXiv  Detail & Related papers  (2021-07-09T17:59:30Z)
• Emerging Properties in Self-Supervised Vision Transformers [57.36837447500544]
  We show that self-supervised ViTs provide new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets) We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base.
  arXiv  Detail & Related papers  (2021-04-29T12:28:51Z)
• DeepViT: Towards Deeper Vision Transformer [92.04063170357426]
  Vision transformers (ViTs) have been successfully applied in image classification tasks recently. We show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper. We propose a simple yet effective method, named Re-attention, to re-generate the attention maps to increase their diversity.
  arXiv  Detail & Related papers  (2021-03-22T14:32:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.