# Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images

arXiv: https://arxiv.org/abs/2112.05786 · HTML: https://mlsanity.com/paper/2112.05786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 30 of 30)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling](https://mlsanity.com/paper/1902.02102/) (2019)
- [A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning](https://mlsanity.com/paper/1609.04453/) (2016)
- [Clustered Object Detection in Aerial Images](https://mlsanity.com/paper/1904.08008/) (2019)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection](https://mlsanity.com/paper/1909.04972/) (2019)
- [Multi-source weak supervision for saliency detection](https://mlsanity.com/paper/1904.00566/) (2019)
- [Towards Precise End-to-end Weakly Supervised Object Detection Network](https://mlsanity.com/paper/1911.12148/) (2019)
- [Weakly Supervised Object Detection with Segmentation Collaboration](https://mlsanity.com/paper/1904.00551/) (2019)
- [Infinite Variational Autoencoder for Semi-Supervised Learning](https://mlsanity.com/paper/1611.07800/) (2016)
- [Two-Phase Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1708.02108/) (2017)
- [Soft Proposal Networks for Weakly Supervised Object Localization](https://mlsanity.com/paper/1709.01829/) (2017)
- [Generalized Product Quantization Network for Semi-supervised Image Retrieval](https://mlsanity.com/paper/2002.11281/) (2020)
- [Weakly Supervised Object Localization Using Things and Stuff Transfer](https://mlsanity.com/paper/1703.08000/) (2017)
- [Semi-Supervised Generation with Cluster-aware Generative Models](https://mlsanity.com/paper/1704.00637/) (2017)
- [Satellite Imagery Multiscale Rapid Detection with Windowed Networks](https://mlsanity.com/paper/1809.09978/) (2018)
- [Adversarial Variational Embedding for Robust Semi-supervised Learning](https://mlsanity.com/paper/1905.02361/) (2019)
- [Zero-Annotation Object Detection with Web Knowledge Transfer](https://mlsanity.com/paper/1711.05954/) (2017)
- [A Comparison of Deep Learning Object Detection Models for Satellite Imagery](https://mlsanity.com/paper/2009.04857/) (2020)
- [Deep Learning for Recognizing Mobile Targets in Satellite Imagery](https://mlsanity.com/paper/2010.06520/) (2020)

## Abstract (arXiv)

The increasing availability and accessibility of numerous overhead images allows us to estimate and assess the spatial arrangement of groups of geospatial target objects, which can benefit many applications, such as traffic monitoring and agricultural monitoring. Spatial arrangement estimation is the process of identifying the areas which contain the desired objects in overhead images. Traditional supervised object detection approaches can estimate accurate spatial arrangement but require large amounts of bounding box annotations. Recent semi-supervised clustering approaches can reduce manual labeling but still require annotations for all object categories in the image. This paper presents the target-guided generative model (TGGM), under the Variational Auto-encoder (VAE) framework, which uses Gaussian Mixture Models (GMM) to estimate the distributions of both hidden and decoder variables in VAE. Modeling both hidden and decoder variables by GMM reduces the required manual annotations significantly for spatial arrangement estimation. Unlike existing approaches that the training process can only update the GMM as a whole in the optimization iterations (e.g., a "minibatch"), TGGM allows the update of individual GMM components separately in the same optimization iteration. Optimizing GMM components separately allows TGGM to exploit the semantic relationships in spatial data and requires only a few labels to initiate and guide the generative process. Our experiments shows that TGGM achieves results comparable to the state-of-the-art semi-supervised methods and outperforms unsupervised methods by 10% based on the F1 scores, while requiring significantly fewer labeled data.