1,082 datasets found

An N-object dataset for testing the proposed framework

The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

A dataset of 650 machine learning models presented in academic publications and relevant gray literature.

Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models.

CLEVR images contain objects characterized by a set of attributes (shape, color, size and material). The questions are grouped into 5 categories: Exist, Count, CompareInteger,...

RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained...

We propose DisCo-CLIP, a distributed memory-efficient CLIP training approach, to reduce the memory consump- tion of contrastive loss when training contrastive learning models.

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such...

The dataset used in the paper is a benchmark dataset for vision recognition, consisting of 10 datasets: Tiny ImageNet, Caltech-256, Flowers-102, Food-101, CIFAR-100, CIFAR-10,...

Two publicly available datasets for training and testing: Scene Flow datasets and KITTI2015 dataset

Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training.

CUB-200 is a fine-grained image classification dataset containing high resolution images of 200 different bird species.

The Waymo Open Motion Dataset is a large-scale dataset for autonomous driving, containing 104,000 20-second frames of driving scenarios marked at 10 Hz.

80 CT-scans and related label data found in various public sources were considered for training and testing of U-Net architectures.

Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine “next-scale prediction” or...

The dataset FAUST contains 10 different bodies in 10 different poses. The irregular surface meshes have 6890 vertices. The dataset is used to evaluate the reconstruction quality...

The dataset used in the paper is a real-world 3D point cloud dataset, which is used for 3D shape classification, part segmentation, and shape retrieval tasks.

A point cloud rendering method that achieves comparable rendering performance to NeRF.