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Found 4 papers, 3 papers with code
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances.
Ranked #1 on
Small Object Detection
on SODA-D
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Besides, meta-data-driven meta-loss objective combined with DAC-MR is capable of achieving better meta-level generalization.
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively.
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics.
Ranked #3 on
Image Classification
on WebVision-1000
