71 datasets found

Yolact dataset contains 100,000 images with 500,000 annotated instances.

The dataset used in the paper is ScanNetV2, a real-world dataset for indoor scenes. It contains 1205 training scenes and 312 testing scenes, with instance-level object bounding...

The Cityscapes dataset is a large-scale urban scene dataset containing over 25,000 images.

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 the paper is MS COCO, Cityscapes, and CTW1500. The authors used these datasets for instance segmentation and few-shot instance segmentation tasks.

OneFormer is a universal image segmentation framework jointly trained on the panoptic, semantic, and instance segmentation tasks.

Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works...

This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision.

LVIS: A dataset for large vocabulary instance segmentation.

The dataset used for testing the proposed unsupervised pre-training method for query-based end-to-end instance segmentation (QEIS) models.

The MHP dataset contains images of humans with annotated instance segmentation masks.

The CCP dataset contains images of clothing with annotated instance segmentation masks.

Instance segmentation by refining object detection with semantic and direction features.

Augmented reality meets deep learning for instance segmentation in urban scenes.

Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts – a detection...

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

A new dataset for grape diseases object detection and instance segmentation.

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

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

The dataset used for large-vocabulary semantic segmentation, including ADE20K, Cityscapes, and COCO-Stuff.