We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection.
## Overview
The paper tackles object detection without predefined anchors (sliding windows). 
The paper is well motivated and existing detection methods often rely on anchors, which may limits their potential. So this paper is solving an interesting problem and seems novel.
The paper provides experiments to support the proposed architecture empirically.
## Summary of the contribution:
1. The paper proposed an object detection approach called FoveaBox that does not rely on anchors (sliding widows). 
2. The paper shows that FoveaBox outperforms some existing object detection methods.
3. The paper shows that FoveaBox can also be used for object proposals by changing the classification target to class-agnostic head.
## My feedback:
I think the paper is well motivated and has value to the object detection community. So I am leaning positive to accept this paper. The major reasons are:
1. The paper studies an interesting architecture which does not rely on sliding windows and shows its effectiveness. The problem seems not very well studied in the existing object detection literature.
2. The paper provides sufficient ablation study to analyze and understand the proposed methods.
However, I believe the paper could be significantly improved especially in the writing. So my position is not strong.
## Improvements
1. The writing can be significantly improved. The paper reads a bit confusing and unclear especially at the technical part. The paper could benefit from a clear overview figure about the proposed approach. Figure 4 seems to be doing that illustration but giving only the tensor shape seems quite confusing. I also listed some questions below about the actual algorithm.
2. The paper could be improved with experiments and ablation on another dataset. Current ablation shows that \eta should be 2.0 for best performance but without a second dataset it is hard to say this value is general. So one may have to tune this parameters in different datasets.
3. The scale is still discretized. So that is essentially anchors in the scale space. I wonder how could the approach applies to scale to?
## Questions
1. How would the proposed approach address the issue when there are multiple bounding boxes around the same pixels? It seems the current approach is predicting a box per pixel?
2. What is the inference computation cost? And do you have a comparison with existing methods on the compute cost?