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# System prepended metadata

title: NeurIPS 2022 papers

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# NeurIPS 2022 papers

## Attention/Transformers

[Fast vision transformers with HiLo attention ](https://openreview.net/forum?id=Pyd6Rh9r1OT) : 
![](https://i.imgur.com/UjzDyoX.png)

- novel self-attention mechanism HiLo, inspired by the insight that high frequencies in an image capture local fine details and low frequencies focus on global structures, whereas a multi-head self-attention layer neglects the characteristic of different frequencies.
- disentangle the high/low frequency patterns in an attention layer by separating the heads into two groups, where one group encodes high frequencies via self-attention within each local window, and another group performs the attention to model the global relationship between the average-pooled low-frequency keys from each window and each query position in the input feature map.
- Hi-Fi : 2x2 windows (or 4x4)  
- [(code)](https://github.com/ziplab/LITv2.)

[Contrastive Adapters for Foundation Model Group Robustness](https://openreview.net/forum?id=uPdS_7pdA9p):
- contrastive learning framework + hard sampling as a layer on top of pretrained model. 
![](https://i.imgur.com/TBpaU3U.png)
- group robustness: reduce gap in performances across classes (but average is not necessarily better)

[A Fast Post-Training Pruning Framework for Transformers](https://openreview.net/forum?id=0GRBKLBjJE)

[Optimizing Relevance Maps of Vision Transformers Improves Robustness](https://openreview.net/forum?id=upuYKQiyxa_)
## Satellite imagery

[Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution](https://openreview.net/forum?id=DEigo9L8xZA)

## RegAg