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Found 7 papers, 2 papers with code
3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking.
Embracing Annotation Efficient Learning (AEL) for Digital Pathology and Natural Images
Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively.
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
Our experiments show that patch classification performance can be improved by manipulating both the image and input resolution in annotation-scarce and annotation-rich environments.
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
Furthermore, we show that models trained with scribble labels yield the same performance boost as full pixel-wise segmentation labels despite being significantly easier and faster to collect.
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling.
Ranked #2 on
Image Retrieval
on CARS196
Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations.
