# G-CNN: an Iterative Grid Based Object Detector

arXiv: https://arxiv.org/abs/1512.07729 · HTML: https://mlsanity.com/paper/1512.07729/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2015.

## Built on by (most cited first, 13 of 13)

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- [RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View](https://mlsanity.com/paper/2110.00644/) (2021)
- [Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection](https://mlsanity.com/paper/2202.05941/) (2022)
- [A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection](https://mlsanity.com/paper/2305.09407/) (2023)
- [Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals](https://mlsanity.com/paper/2409.07973/) (2024)
- [Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection](https://mlsanity.com/paper/2507.07994/) (2025)

## Abstract (arXiv)

We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.