Deep features are a cornerstone of computer vision research, capturing image
semantics and enabling the community to solve downstream tasks even in the
zero- or few-shot regime. However, these features often lack the spatial
resolution to directly perform dense prediction tasks like segmentation and
depth prediction because models aggressively pool information over large areas.
In this work, we introduce FeatUp, a task- and model-agnostic framework to
restore lost spatial information in deep features. We introduce two variants of
FeatUp: one that guides features with high-resolution signal in a single
forward pass, and one that fits an implicit model to a single image to
reconstruct features at any resolution. Both approaches use a multi-view
consistency loss with deep analogies to NeRFs. Our features retain their
original semantics and can be swapped into existing applications to yield
resolution and performance gains even without re-training. We show that FeatUp
significantly outperforms other feature upsampling and image super-resolution
approaches in class activation map generation, transfer learning for
segmentation and depth prediction, and end-to-end training for semantic
segmentation.

使用一个多角度一致性损失函数，我们提出了 FeatUp，一种可以恢复深度特征中丢失空间信息的任务和模型无关的框架。通过保持原始语义，FeatUp 在现有应用中替换特征，从而取得了分辨率和性能上的提升，甚至无需重新训练，我们在类别激活图、分割和深度预测的传递学习以及语义分割的端到端训练方面展示了 FeatUp 明显优于其他特征上采样和图像超分辨率方法。