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Found 4 papers, 1 papers with code
Exploiting Shape Cues for Weakly Supervised Semantic Segmentation
To this end, previous methods adopt the common pipeline: they generate pseudo masks from class activation maps (CAMs) and use such masks to supervise segmentation networks.
Cut and Continuous Paste towards Real-time Deep Fall Detection
Deep learning based fall detection is one of the crucial tasks for intelligent video surveillance systems, which aims to detect unintentional falls of humans and alarm dangerous situations.
Contrastive Attention Maps for Self-Supervised Co-Localization
The goal of unsupervised co-localization is to locate the object in a scene under the assumptions that 1) the dataset consists of only one superclass, e. g., birds, and 2) there are no human-annotated labels in the dataset.
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization
Furthermore, we propose foreground consistency loss that penalizes earlier layers producing noisy attention regarding the later layer as a reference to provide them with a sense of backgroundness.
