249 datasets found

Detecting objects from aerial images poses significant challenges due to the following factors: 1) Aerial images typically have very large sizes, generally with millions or even...

The Behave dataset contains various scenes with human-object interactions, and is used to evaluate the proposed object-level 3D semantic mapping approach.

The T-Less dataset is used for testing the class-adaptive object detector. It contains 15 objects with a large number of occurrences.

The HomeBrew dataset is used for testing the class-adaptive object detector. It contains 30 objects with a large number of occurrences.

The YCB-V dataset is used for testing the class-adaptive object detector. It contains 33 objects with a large number of occurrences.

The FewSol dataset is used for training the class-adaptive object detector. It contains 666 objects with a large number of occurrences.

The DoPose and HOPE datasets are used for testing the class-adaptive object detector. The DoPose dataset contains 18 objects with distinctive shapes and colors, while the HOPE...

Selective search is a method for object detection.

Deeply Supervised Object Detector (DSOD) is a framework that can learn object detectors from scratch.

The ADL Dataset is a public dataset used for object detection in wearable videos. It contains 27000 frames extracted from 10 hours of video recorded with a chest-mounted GoPro...

The OpenImagesV4 dataset is a large benchmark dataset for object detection and image classification. It contains 1.7 million images with 1,000 object classes.

Semantic segmentation is one of the most fundamental tasks that aims to classify every pixel of a given image into a specific class. It is widely applied to many applications...

FoveaBox is a completely anchor-free framework for generic object detection. It directly predicts the object existing possibility and the corresponding boundary for each...

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.

iSAID dataset is a large-scale high-resolution remote sensing dataset for aerial instance segmentation, containing many small objects with complex backgrounds.

The PASCAL VOC 2007, 2010, 2012 datasets, the ILSVRC 2013 dataset, and the MSCOCO 2014 dataset.

A min-entropy latent model (MELM) for weakly supervised object detection.

The MSCOCO dataset is a large-scale object detection dataset.

A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification.

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