13 datasets found

The dataset used in this paper is a collection of decimal addition tasks, where the input lengths range from 1 to 40 digits. The dataset is used to evaluate the ability of...

Point-BERT is a new paradigm for learning point cloud Transformers. It pre-trains standard point cloud Transformers with a Masked Point Modeling (MPM) task.

Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations.

3D-retr: End-to-end single and multi-view 3D reconstruction with transformers

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

Convolutional neural networks (CNNs) are ubiquitous in computer vision, with a myriad of effective and efficient variations. Recently, Transformers – originally introduced in...

Medical image segmentation remains particularly challenging for complex and low-contrast anatomical structures. In this paper, we introduce the U-Transformer network, which...

Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them...

The dataset used in the paper is a visual reasoning task using Graph Neural Networks (GNNs) and Recurrent Independent Mechanisms (RIMs). The dataset consists of 8 Atari games...

A dataset for training transformers to perform tasks such as language translation and text generation.

The dataset is a comprehensive review of over 100 transformer methods for different 3D vision tasks, including classification, segmentation, detection, completion, pose...

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

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...