38 datasets found

The PersonLab dataset is a benchmark for person pose estimation and instance segmentation. It contains 20,000 images with 80,000 annotated person instances.

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

Po-LarMask: Single shot instance segmentation with polar representation.

Solo: Segmenting objects by locations.

Microsoft COCO: common objects in images.

Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and...

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.

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

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.

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.

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.

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