SNIPER is an efficient multi-scale training approach for object detection that selectively processes context regions called chips around ground-truth objects, enabling larger batch sizes and GPU-efficient training with batch normalization. AutoFocus is an efficient multi-scale inference algorithm that adopts a coarse-to-fine approach, predicting category-agnostic segmentation maps to identify regions likely to contain small objects before processing them at finer scales.