human label maps human dense poses Along with model and garment image pair, we provide also the keypoints, skeleton, human label map, and dense pose. More info Keypoints For all image pairs of the dataset, we stored the joint coordinates of human poses. In particular, we usedOpenPose[1]...
Image classification refers to the task of assigning a label or tag to an image. Typically supervised deep learning algorithms are used for Image Classification tasks and are trained on images annotated with a label chosen from a fixed set of predefined labels. Annotations required for image clas...
A novel gesture discriminator embedded into the generator is used to calculate the completeness of skeleton pose images. The improved generator can make generated images more realistic, which would be conducive to feature extraction. The role of the constrained two-stage fusion network is to extract...
<tool id="imagej2_noise" name="Add or remove noise" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0"> <description>with ImageJ2</description> <macros> <import>imagej2_macros.xml</import> <xml name="insertion_select"> <param name="insertion" type="select" label="Inser...
The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image ...
2814 CROSS-TRAINING DEEP NEURAL NETWORKS FOR LEARNING FROM LABEL NOISE 1730 CYCLONE INTENSITY ESTIMATE WITH CONTEXT-AWARE CYCLEGAN 2566 DANET: DEPTH-AWARE NETWORK FOR CROWD COUNTING 1096 DATA AUGMENTATION VIA IMAGE REGISTRATION 1623 DATASET CULLING: TOWARDS EFFICIENT TRAINING OF DISTILLATION-BASED DOMAIN ...
In either case, Bellybutton generates two labels for each pixel: a binary classification label that corresponds to ‘innie’ or ‘outie’, which does not distinguish between uniquely labeled regions, and a scalar label, distance (in pixels) to the nearest edge of a region. It is these two ...
Along with the birth and development of ImageNet, the world-famous “ImageNet Large Scale Visual Recognition Challenge (ILSVRC)” [7] began in 2010. In the competition, two evaluation measures are used: Top-1 error rate (for a certain image, whether the label with top one probability predic...
In this work, the main objective is to accurately recover the 3D human poses with a calibrated monocular camera, where the 3D human motion is represented by a skeleton model parameterized by joint positions. Our proposed framework consists of three key components, namely, height-map generation, ...
Currently, no single algorithm exists that can match the multi-label classification skills of a clinician across a variety of clinical scenarios. Nonetheless, significant progress has been made in tumour segmentation within18F-FDG PET/CT imaging [24,25,26], driven by deep learning frameworks like ...