453 datasets found

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as...

The CHAMELEON dataset is a dataset for camouflaged object detection.

The COD10K dataset is a large-scale dataset for camouflaged object detection.

RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained...

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

The FSS-1000 dataset contains 1000 images with 1000 object categories.

Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the...

The dataset used in the paper is PASCAL VOC2007 test dataset and VOC2012 trainval datasets.

Large-scale vision-and-language models trained on curated and web-scrapped data have led to significant improvements over task-specific models when transferred to downstream...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

Semantic segmentation is one of the fundamental prob-lems in computer vision, whose task is to assign a seman-tic label to each pixel of an image so that different classes can...

NeRF [33] has demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray...

The COCO2017 dataset is used for the trainability experiment, which includes 47,429 images with 210,893 objects.

The COCO-Thing-Stuff dataset is used for the L2I task, which includes 118,287 training images and 5,000 validation images. Each image is annotated with bounding boxes and...

The CornerNet dataset is a single-stage object detection benchmark.

Grid R-CNN is a novel object detection framework that adopts a grid guided localisation mechanism for accurate object detection.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

Instance segmentation (IS) is an important computer vision task, aiming at simultaneously predicting the class label and the binary mask for each instance of interest in an image.