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Found 3 papers, 2 papers with code
BEV-CLIP: Multi-modal BEV Retrieval Methodology for Complex Scene in Autonomous Driving
The demand for the retrieval of complex scene data in autonomous driving is increasing, especially as passenger vehicles have been equipped with the ability to navigate urban settings, with the imperative to address long-tail scenarios.
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Unlike previous work, MADAug selects augmentation operators for each input image by a model-adaptive policy varying between training stages, producing a data augmentation curriculum optimized for better generalization.
Subclass-balancing Contrastive Learning for Long-tailed Recognition
We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses.
