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Found 4 papers, 1 papers with code
Improving Dense Contrastive Learning with Dense Negative Pairs
Many contrastive representation learning methods learn a single global representation of an entire image.
A Study on Self-Supervised Object Detection Pretraining
In this work, we study different approaches to self-supervised pretraining of object detection models.
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
While positive pairs can be generated reliably (e. g., as different views of the same image), it is difficult to accurately establish negative pairs, defined as samples from different images regardless of their semantic content or visual features.
3D Hand Pose Detection in Egocentric RGB-D Images
We focus on the task of everyday hand pose estimation from egocentric viewpoints.
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