71 datasets found

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.

SA-1B is a large-scale benchmark for instance segmentation and object detection.

LVIS is a large-scale benchmark for instance segmentation and object detection.

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 COCO 2017 Dataset is a large-scale benchmark dataset for object detection, semantic segmentation, and instance segmentation.

The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich...

The PartNet dataset is a large-scale benchmark for 3D object recognition and retrieval. It consists of 527 objects composed of 2690 parts, which can be composited into a...

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.

Thai car-damage dataset for 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.