GF-FRCNN MSCOCO
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://data.mendeley.com/datasets/sf238jg557
下载链接
链接失效反馈官方服务:
资源简介:
Correction in Version 1: Each file contains 36 x 17 dimension features extracted from fixed set 36 bounding boxes (Anderson et al., 2018) of Faster R-CNN (Ren et al., 2015) for each image in the MSCOCO (Lin et al., 2014) Image Captioning Dataset. The sequence is: relative_x1, relative_y1, relative_x2, relative_y2, relative_widthb, relative_heightb, relative_area, aspect_ratio, relative_center_x, relative_center_y, relative_perimeter, relative_diagonal, relative_left_margin, relative_top_margin, relative_right_margin, relative_bottom_margin, relative_distance_to_center.
Correction in Version 2: Wrong uploaded
Version 3 : This version contains 17 features extracted from the adaptive set 10 to 100 bounding boxes (Anderson et al., 2018) of Faster R-CNN (Ren et al., 2015) for each image in the MSCOCO (Lin et al., 2014) Image Captioning Dataset. This dataset, containing all the possible geometric features extracted from 123,287 images in the MSCOCO image captioning dataset (Lin et al., 2014) , provides essential spatial information about each object. The sequence is: relative_x1, relative_y1, relative_x2, relative_y2, relative_widthb, relative_heightb, relative_area, aspect_ratio, relative_center_x, relative_center_y, relative_perimeter, relative_diagonal, relative_left_margin, relative_top_margin, relative_right_margin, relative_bottom_margin, relative_distance_to_center.
References
Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster r-cnn: Towards realtime object detection with region proposal networks. Advances in neural
information processing systems 28.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014. Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer. pp. 740–755.
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L., 2018. Bottom-up and top-down attention for image captioning and visual question answering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6077–6086.
创建时间:
2024-11-18



