Variational Feature Pyramid Networks

BibTeX


@InProceedings{pmlr-v162-dimitrakopoulos22a,
title = {Variational Feature Pyramid Networks},
author = {Dimitrakopoulos, Panagiotis and Sfikas, Giorgos and Nikou, Christophoros},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {5142--5152},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/dimitrakopoulos22a/dimitrakopoulos22a.pdf},
url = {https://proceedings.mlr.press/v162/dimitrakopoulos22a.html},
abstract = {Recent architectures for object detection adopt a Feature Pyramid Network as a backbone for deep feature extraction. Many works focus on the design of pyramid networks which produce richer feature representations. In this work, we opt to learn a dataset-specific architecture for Feature Pyramid Networks. With the proposed method, the network fuses features at multiple scales, it is efficient in terms of parameters and operations, and yields better results across a variety of tasks and datasets. Starting by a complex network, we adopt Variational Inference to prune redundant connections. Our model, integrated with standard detectors, outperforms the state-of-the-art feature fusion networks.}
}