14 citations · 23 across the 6 of their papers we have counts for
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cs.CV2022★ 6 cited
An Impartial Take to the CNN vs Transformer Robustness Contest
Francesco Pinto, Philip H. S. Torr, Puneet K. Dokania
Following the surge of popularity of Transformers in Computer Vision, several studies have attempted to determine whether they could be more robust to distribution shifts and provi…
cs.CV2022★ 1 cited
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Tom Joy, Francesco Pinto, Ser-Nam Lim +2
It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for t…