A more recent 2024 paper titled introduces a different "BBAM".
It uses a trained object detector to find the "smallest area" of an image that makes the detector produce the same result, effectively creating a map that identifies the object within the box.
Published at CVPR 2021 (Conference on Computer Vision and Pattern Recognition). BBAM.rar
The most prominent paper using this acronym is .
This research focuses on Weakly Supervised Learning (WSL) , where the goal is to perform complex tasks like pixel-level segmentation using only simple bounding box labels rather than expensive pixel-by-pixel annotations. A more recent 2024 paper titled introduces a
You can find the full PDF and supplementary materials on arXiv or through the CVF Open Access portal. 2. S²ML²-BBAM: Balanced Binary Angular Margin
This paper deals with Semi-supervised Multi-label Learning (SSMLL) , which tries to train models when only a portion of the data has multiple labels. The most prominent paper using this acronym is
Sometimes specialized datasets related to the papers above are shared this way.