26 datasets found

The proposed approach is validated using various state-of-the-art network architectures (VGG and ResNet) on several well-known object detection benchmarks (i.e., PASCAL VOC 2007...

The OpenImages dataset V6 contains images with annotations for object detection and instance segmentation tasks.

The SUNRGB-D dataset features 5,285 training and 5,050 test samples from multiple RGB-D cameras. The dataset provides annotations for the first 37 NYUv2 semantic classes and for...

Microsoft COCO: common objects in images.

The dataset used in the paper is the COCO object detection and instance segmentation dataset, and the ADE20K semantic segmentation dataset.

The COCO 2014 validation set is used for evaluating the performance of the proposed method.

The dataset used in this paper is a collection of frames for video coding, with different Quantisation Parameters (QPs) and frame types.

The dataset used in this paper for training and testing the KAISA optimizer framework.

LVIS is a large-vocabulary object detection and instance segmentation dataset.

The dataset used in the paper is ImageNet-1K and MS COCO.

The COCO-LT dataset is a long-tail version of the COCO dataset.

The Cityscapes dataset consists of 8 thing classes and 11 stuff classes.

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

The COCO test-dev dataset is used for instance segmentation. It contains 20k test-dev images.

A point cloud dataset with large quantity of dynamic instances, consisting of 2,988 real-world scans with point-level annotations.

Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate...

Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

Object detection is one of the most foundational computer vision task and is essential for many real-world applications. The object detection pipeline has been developed...

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

The COCO2017 dataset is used for the trainability experiment, which includes 47,429 images with 210,893 objects.