  Post-hoc explanation methods have often been criticised for abstracting away
the decision-making process of deep neural networks. In this work, we would
like to provide natural language descriptions for what different layers of a
vision backbone have learned. Our DeViL method decodes vision features into
language, not only highlighting the attribution locations but also generating
textual descriptions of visual features at different layers of the network. We
train a transformer network to translate individual image features of any
vision layer into a prompt that a separate off-the-shelf language model decodes
into natural language. By employing dropout both per-layer and
per-spatial-location, our model can generalize training on image-text pairs to
generate localized explanations. As it uses a pre-trained language model, our
approach is fast to train, can be applied to any vision backbone, and produces
textual descriptions at different layers of the vision network. Moreover, DeViL
can create open-vocabulary attribution maps corresponding to words or phrases
even outside the training scope of the vision model. We demonstrate that DeViL
generates textual descriptions relevant to the image content on CC3M surpassing
previous lightweight captioning models and attribution maps uncovering the
learned concepts of the vision backbone. Finally, we show DeViL also
outperforms the current state-of-the-art on the neuron-wise descriptions of the
MILANNOTATIONS dataset. Code available at
https://github.com/ExplainableML/DeViL
