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Found 6 papers, 2 papers with code
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Multi Label Text Classification
Multi-Label Text Classification
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High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification
Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images.
Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis
The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e. g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Robust Scene Text Recognition Using Sparse Coding based Features
For character detection, we use the HSC features instead of using the Histograms of Oriented Gradients (HOG) features.
