Oct 3, 2014 · Part-based R-CNNs for Fine-grained Category Detection Caltech-UCSD bird dataset (CUB200-2011) with ~12,000 images of 200 bird species. Strongly supervised setting …

.com Abstract This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for obj. ct detection. Fast R-CNN builds on previous work to efficiently classify ob-ject …

The proposed model leverages Fast R-CNN for its efficiency in detecting and classifying objects within each image. To enhance the model’s ability to focus on relevant elements within these …

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern …

Generates high-quality segmentation mask Model does Object Detection, Instance Segmentation and can also be extended to human pose estimation!!!!!! All of them are done in parallel …

The objective of this work is to provide a comparative analysis of the YOLO and Faster R-CNN models in traffic object detection using the KITTI dataset as a representative analysis platform.

In this paper, we answer this question by developing a detector that uses a trivial region generation scheme, constant for each image.