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Found 3 papers, 3 papers with code
Semi-supervised Open-World Object Detection
We demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection, proposing carefully curated splits and baseline performance evaluations.
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
However, the CNN feature maps still maintain the spatial relationship and we utilize this property to design self-supervised learning approaches to train the encoder of object detection transformers in pretraining and multi-task learning settings.
MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages
These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e. g., SQuAD).
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