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Found 4 papers, 0 papers with code
Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition
In image recognition, both foreground (FG) and background (BG) play an important role; however, standard deep image recognition often leads to unintended over-reliance on the BG, limiting model robustness in real-world deployment settings.
FungiTastic: A multi-modal dataset and benchmark for image categorization
We introduce a new, challenging benchmark and a dataset, FungiTastic, based on fungal records continuously collected over a twenty-year span.
Single Image Test-Time Adaptation for Segmentation
Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation.
Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures
These are used within an assisted labeling framework where the annotators can interact with them as proposal segmentation masks by deciding to accept, reject or modify them, and interactions are logged as weak labels to further refine the classifier.
