# CS-R-FCN: Cross-supervised Learning for Large-Scale Object Detection

arXiv: https://arxiv.org/abs/1905.12863 · HTML: https://mlsanity.com/paper/1905.12863/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 1 of 1)

- [Towards Establishing Systematic Classification Requirements for Automated Driving](https://mlsanity.com/paper/2307.14058/) (2023)

## Abstract (arXiv)

Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories. In this paper, we present a novel cross-supervised learning pipeline for large-scale object detection, denoted as CS-R-FCN. First, we propose to utilize the data flow of image-level annotated images in the fully-supervised two-stage object detection framework, leading to cross-supervised learning combining bounding-box-level annotated data and image-level annotated data. Second, we introduce a semantic aggregation strategy utilizing the relationships among the cross-supervised categories to reduce the unreasonable mutual inhibition effects during the feature learning. Experimental results show that the proposed CS-R-FCN improves the mAP by a large margin compared to previous related works.