Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown the same or better …
This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object …
Identifying and characterizing vascular plants in time and space is required in various disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …
H Chefer, S Gur, L Wolf - … of the IEEE/CVF conference on …, 2021 - openaccess.thecvf.com
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In …
Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …
T Zhou, M Zhang, F Zhao, J Li - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Learning semantic segmentation from weakly-labeled (eg, image tags only) data is challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
Mining precise class-aware attention maps, aka, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
Light traveling through water results in strong scattering across color channels, restricting visibility in underwater images. Many cutting-edge underwater image enhancement …