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Found 7 papers, 3 papers with code
Improving and Assessing Anomaly Detectors for Large-Scale Settings
We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
Scaling Out-of-Distribution Detection for Real-World Settings
We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
Learning Rich Representations For Structured Visual Prediction Tasks
We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Regularizing Deep Networks by Modeling and Predicting Label Structure
Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations.
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories.
Feedforward semantic segmentation with zoom-out features
We introduce a purely feed-forward architecture for semantic segmentation.
