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Found 6 papers, 2 papers with code
One-bit Supervision for Image Classification: Problem, Solution, and Beyond
An intriguing property of the setting is that the burden of annotation largely alleviates in comparison to offering the accurate label.
Domain-Agnostic Prior for Transfer Semantic Segmentation
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community.
One-Bit Active Query With Contrastive Pairs
The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling.
Vibration-based Uncertainty Estimation for Learning from Limited Supervision
We investigate the problem of estimating uncertainty for training data, so that deep neural networks can make use of the results for learning from limited supervision.
One-bit Supervision for Image Classification
Instead of training a model upon the accurate label of each sample, our setting requires the model to query with a predicted label of each sample and learn from the answer whether the guess is correct.
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval.
