# SGE net: Video object detection with squeezed GRU and information entropy map

arXiv: https://arxiv.org/abs/2106.07224 · HTML: https://mlsanity.com/paper/2106.07224/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Learning Correspondence from the Cycle-Consistency of Time](https://mlsanity.com/paper/1903.07593/) (2019)
- [Deep Feature Flow for Video Recognition](https://mlsanity.com/paper/1611.07715/) (2016)
- [Seq-NMS for Video Object Detection](https://mlsanity.com/paper/1602.08465/) (2016)
- [Video Object Detection with an Aligned Spatial-Temporal Memory](https://mlsanity.com/paper/1712.06317/) (2017)
- [Mobile Video Object Detection with Temporally-Aware Feature Maps](https://mlsanity.com/paper/1711.06368/) (2017)
- [Convolutional Gated Recurrent Networks for Video Segmentation](https://mlsanity.com/paper/1611.05435/) (2016)
- [NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation](https://mlsanity.com/paper/1907.11320/) (2019)
- [Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking](https://mlsanity.com/paper/1607.05781/) (2016)
- [Impression Network for Video Object Detection](https://mlsanity.com/paper/1712.05896/) (2017)
- [Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation](https://mlsanity.com/paper/2001.08869/) (2020)
- [SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation](https://mlsanity.com/paper/2009.12473/) (2020)

## Abstract (arXiv)

Recently, deep learning based video object detection has attracted more and more attention. Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information. The RNN-based algorithm is an effective way to enhance detection performance in videos with temporal information. However, most studies in this area only focus on accuracy while ignoring the calculation cost and the number of parameters. In this paper, we propose an efficient method that combines channel-reduced convolutional GRU (Squeezed GRU), and Information Entropy map for video object detection (SGE-Net). The experimental results validate the accuracy improvement, computational savings of the Squeezed GRU, and superiority of the information entropy attention mechanism on the classification performance. The mAP has increased by 3.7 contrasted with the baseline, and the number of parameters has decreased from 6.33 million to 0.67 million compared with the standard GRU.