# iffDetector: Inference-aware Feature Filtering for Object Detection

arXiv: https://arxiv.org/abs/2006.12708 · HTML: https://mlsanity.com/paper/2006.12708/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Dual-stream Network for Visual Recognition](https://mlsanity.com/paper/2105.14734/) (2021)
- [UniHead: Unifying Multi-Perception for Detection Heads](https://mlsanity.com/paper/2309.13242/) (2023)
- [PAFNet: An Efficient Anchor-Free Object Detector Guidance](https://mlsanity.com/paper/2104.13534/) (2021)
- [Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections](https://mlsanity.com/paper/2105.03139/) (2021)

## Abstract (arXiv)

Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic Inference-aware Feature Filtering (IFF) module that can easily be combined with modern detectors, resulting in our iffDetector. Unlike conventional open-loop feature calculation approaches without feedback, the IFF module performs closed-loop optimization by leveraging high-level semantics to enhance the convolutional features. By applying Fourier transform analysis, we demonstrate that the IFF module acts as a negative feedback that theoretically guarantees the stability of feature learning. IFF can be fused with CNN-based object detectors in a plug-and-play manner with negligible computational cost overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that our iffDetector consistently outperforms state-of-the-art methods by significant marginsfootnoteThe test code and model are anonymously available in https://github.com/anonymous2020new/iffDetector .