10 datasets found

The dataset used in this paper for analyzing the memory cost of three machine learning algorithms: transformers, spatial convolution, and FFT.

A novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced complexity compared to...

Semantic segmentation is a fundamental task in computer vision and enables many downstream applications. It is related to image classification since it produces per-pixel...

A new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are...

The dataset used in this paper is a pre-trained BERT model trained on English Wikipedia and Books datasets.

The TransFusion model is a robust LiDAR-camera fusion for 3D object detection with transformers.

Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency.

PointConvFormer is a novel point cloud layer that combines ideas from point convolution and transformers.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

An image is worth 16x16 words: Transformers for image recognition at scale.