ConvNeXt-S model with SimPool (gamma=2.0) trained on ImageNet-1k for 100 epochs. Self-supervision with DINO.
SimPool is a simple attention-based pooling method at the end of network, introduced on this ICCV 2023 paper and released in this repository.
Disclaimer: This model card is written by the author of SimPool, i.e. Bill Psomas.
Motivation
Convolutional networks and vision transformers have different forms of pairwise interactions, pooling across layers and pooling at the end of the network. Does the latter really need to be different?
As a by-product of pooling, vision transformers provide spatial attention for free, but this is most often of low quality unless self-supervised, which is not well studied. Is supervision really the problem?
Method
SimPool is a simple attention-based pooling mechanism as a replacement of the default one for both convolutional and transformer encoders. For transformers, we completely discard the [CLS] token.
Interestingly, we find that, whether supervised or self-supervised, SimPool improves performance on pre-training and downstream tasks and provides attention maps delineating object boundaries in all cases.
One could thus call SimPool universal.
Evaluation with k-NN
k
top1
top5
10
68.66
85.832
20
68.508
87.636
100
66.33
88.876
200
64.93
88.53
BibTeX entry and citation info
@misc{psomas2023simpool,
title={Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?},
author={Bill Psomas and Ioannis Kakogeorgiou and Konstantinos Karantzalos and Yannis Avrithis},
year={2023},
eprint={2309.06891},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{liu2022convnet,
title={A convnet for the 2020s},
author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={11976--11986},
year={2022}
}
