explain why ViTs are indeed more robust learners. For example, with fewer parameters and similar dataset and pre-training combinations, ViT gives a top-1 accuracy of 28.10% on ImageNet-A which is 4.3x higher than a comparable variant of BiT. Our analyses on image masking, Fourier spectrum s...
[36] Paul, S., & Chen, P. Y. (2022, June). Vision transformers are robust learners. InProceedings of the AAAI Conference on Artificial Intelligence(Vol. 36, No. 2, pp. 2071-2081). [37] Qian, Y., Wang, J., Wang, B., Zeng, S., Gu, Z., Ji, S., & Swaileh, W. (2020...
1、SATA: "Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets", WACV, 2023 2、SparK: "Sparse and Hierarchical Masked Modeling for Convolutional Representation Learning", ICLR, 2023 3、MOAT: "MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models", ICL...
This repository contains the code for the paperVision Transformers are Robust Learnersby Sayak Paul*and Pin-Yu Chen*(AAAI 2022). *Equal contribution. Update December 2022: We won theML Research Spotlight from Kaggle. Update July 2022: The publication is now available as a part of theAAAI-22 ...
research computer-vision deep-learning transformers attention jax vision-transformer Updated Feb 27, 2025 Python towhee-io / towhee Star 3.3k Code Issues Pull requests Discussions Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast. machine-learning ...
Vision Transformers are Robust Learners [paper] [code] Manipulation Detection in Satellite Images Using Vision Transformer [paper] [Segmenter] Segmenter: Transformer for Semantic Segmentation [paper] [code] [Swin-Unet] Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [paper] [code...
Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. Th...
We posit that these drawbacks stem from a shared design flaw: the absence of a sufficiently robust encoder for feature extraction of human-scene interactions. Recently, the pre-trained plain vision transformers (ViTs) [10] have demonstrated remarkable visual modeling capabilities. A few works have ...
Vision Transformers are Robust Learners [paper] [code] Manipulation Detection in Satellite Images Using Vision Transformer [paper] [Swin-Unet] Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [paper] [code] Self-Supervised Learning with Swin Transformers [paper] [code] [SCTN] ...
Vision Transformers are Robust Learners [paper] [code] Manipulation Detection in Satellite Images Using Vision Transformer [paper] [Swin-Unet] Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [paper] [code] Self-Supervised Learning with Swin Transformers [paper] [code] [SCTN] ...