150 datasets found

The dataset used for experiments on generic image retrieval, person re-identification, and low-shot semantic segmentation.

Semantic segmentation is a fundamental task in computer vision and has witnessed great progress using deep learning in the past few years. It aims at segmenting things or stuff...

The proposed framework for crack semantic segmentation.

The proposed framework for road semantic segmentation.

The dataset is used for semantic segmentation, and the authors propose a scalable discretization-and-relaxation (SDR) approach to simulate complex scenes.

The PASCAL Visual Object Classes Challenge (VOC) is a benchmark dataset for object detection and semantic segmentation.

Incremental learning techniques for semantic segmentation

The dataset used in the paper is a synthetic dataset for image-based object detection tasks, specifically instance segmentation and monocular 3D detection.

SemanticKITTI: A dataset for semantic scene understanding of lidar sequences.

Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network...

A dataset for sewer-culvert defect segmentation, featuring pixel-level annotations of nine common structural deficiency classes.

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

The NYU dataset contains 1449 depth maps captured by Kinect.

The SUNCG dataset is a manually created large-scale synthetic scene dataset. It contains 139368 valid pairs of depth map and complete labels for training, and 470 pairs for...

The VOC2012 dataset is a multi-label image recognition dataset. It contains 11,540 train-val images and 10,991 test images.

ADE20K: A Dataset for Semantic Segmentation

Semantic segmentation performs pixel-level classifica- tion to localize objects from different classes in the input image. Open-vocabulary semantic segmentation aims to...

Weakly-supervised semantic segmentation using plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS).

The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.

Semantic segmentation is a fundamental task in many computer vision applications, which assigns a class label to each pixel in the image.