6 datasets found

The ResNet-50 and ResNet-101 are used as the backbone image feature extractor.

Human Attribute Recognition (HAR) is a challenging task due to large variations of body gestures, external occlusions, lighting conditions, image resolutions and blurrinesses.

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

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 Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a...