# SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

arXiv: https://arxiv.org/abs/1808.04974 · HTML: https://mlsanity.com/paper/1808.04974/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection](https://mlsanity.com/paper/2103.17115/) (2021)
- [A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://mlsanity.com/paper/2110.13389/) (2021)
- [RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection](https://mlsanity.com/paper/2208.08738/) (2022)
- [No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects](https://mlsanity.com/paper/2208.03641/) (2022)
- [GraphFPN: Graph Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2108.00580/) (2021)
- [Edge-aware Feature Aggregation Network for Polyp Segmentation](https://mlsanity.com/paper/2309.10523/) (2023)
- [The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions](https://mlsanity.com/paper/2101.07832/) (2021)
- [CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects](https://mlsanity.com/paper/2506.09897/) (2025)

## Abstract (arXiv)

Most of the recent successful methods in accurate object detection build on the convolutional neural networks (CNN). However, due to the lack of scale normalization in CNN-based detection methods, the activated channels in the feature space can be completely different according to a scale and this difference makes it hard for the classifier to learn samples. We propose a Scale Aware Network (SAN) that maps the convolutional features from the different scales onto a scale-invariant subspace to make CNN-based detection methods more robust to the scale variation, and also construct a unique learning method which considers purely the relationship between channels without the spatial information for the efficient learning of SAN. To show the validity of our method, we visualize how convolutional features change according to the scale through a channel activation matrix and experimentally show that SAN reduces the feature differences in the scale space. We evaluate our method on VOC PASCAL and MS COCO dataset. We demonstrate SAN by conducting several experiments on structures and parameters. The proposed SAN can be generally applied to many CNN-based detection methods to enhance the detection accuracy with a slight increase in the computing time.