56 datasets found

The proposed scheme uses IoT based health monitoring and CNN based object detection methods to detect social distancing violations and track exposed or infected people.

MUAD: A synthetic dataset for autonomous driving with multiple uncertainties and annotations for semantic segmentation, depth estimation, object detection, and instance...

Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation.

The BDD100k dataset is a large-scale driving dataset containing 100,000 images of various weather conditions, including clear, rainy, foggy, and night-time.

The dataset used in the paper is the Pascal VOC 2012 dataset, which is a benchmark for instance segmentation. The dataset consists of 1464 images with 20 class categories and...

The MS COCO dataset is a large benchmark for image captioning, containing 328K images with 5 caption descriptions each.

The proposed Win Transformer achieves consistently superior performance than Swin Transformer on multiple computer vision tasks, including image recognition, semantic...

The FBMS dataset contains 29 training videos and 30 test videos.

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used various benchmarks such as ImageNet, ImageNet ReaL, ImageNet V2, etc.

The dataset used in the paper is the ImageNet and MS COCO benchmarks.

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used the ImageNet-1K, COCO, and ADE20K datasets for image classification,...

PASCAL-5i and COCO-20i are datasets used for evaluation of few-shot segmentation.

The UCF QNRF dataset is a crowd counting dataset containing 334 images.

The FSC147 dataset is a large-scale dataset for evaluating class-agnostic counting (CAC) models. It contains 6,135 natural images of 147 object categories.

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The TrackingNet dataset is a benchmark for visual tracking, containing 511 video sequences with varying difficulties.

The NFS30 dataset contains 100 videos and a length of 380K frames in total.

The RefCOCOg dataset is a reconstructed dataset of the MS-COCO dataset, containing 85,474 referring expressions for 54,822 objects in 26,711 images.

The dataset used in the paper is a benchmark for referring expression grounding, containing 142,210 referring expressions for 50,000 referents in 19,994 images.