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Found 6 papers, 4 papers with code
Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
The ability to understand visual information from limited labeled data is an important aspect of machine learning.
Parting with Illusions about Deep Active Learning
Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples.
Essentials for Class Incremental Learning
Contemporary neural networks are limited in their ability to learn from evolving streams of training data.
Localized Vision-Language Matching for Open-vocabulary Object Detection
In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes.
Open-vocabulary Attribute Detection
The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models.
Best Practices in Active Learning for Semantic Segmentation
Active learning is particularly of interest for semantic segmentation, where annotations are costly.
