# RN-VID: A Feature Fusion Architecture for Video Object Detection

arXiv: https://arxiv.org/abs/2003.10898 · HTML: https://mlsanity.com/paper/2003.10898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [FFAVOD: Feature Fusion Architecture for Video Object Detection](https://mlsanity.com/paper/2109.07298/) (2021)
- [STF: Spatio-Temporal Fusion Module for Improving Video Object Detection](https://mlsanity.com/paper/2402.10752/) (2024)

## Abstract (arXiv)

Consecutive frames in a video are highly redundant. Therefore, to perform the task of video object detection, executing single frame detectors on every frame without reusing any information is quite wasteful. It is with this idea in mind that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to video object detection. Our contributions are twofold. First, we propose a new architecture that allows the usage of information from nearby frames to enhance feature maps. Second, we propose a novel module to merge feature maps of same dimensions using re-ordering of channels and 1 x 1 convolutions. We then demonstrate that RN-VID achieves better mean average precision (mAP) than corresponding single frame detectors with little additional cost during inference.