  We introduce Patch Aligned Contrastive Learning (PACL), a modified
compatibility function for CLIP's contrastive loss, intending to train an
alignment between the patch tokens of the vision encoder and the CLS token of
the text encoder. With such an alignment, a model can identify regions of an
image corresponding to a given text input, and therefore transfer seamlessly to
the task of open vocabulary semantic segmentation without requiring any
segmentation annotations during training. Using pre-trained CLIP encoders with
PACL, we are able to set the state-of-the-art on the task of open vocabulary
zero-shot segmentation on 4 different segmentation benchmarks: Pascal VOC,
Pascal Context, COCO Stuff and ADE20K. Furthermore, we show that PACL is also
applicable to image-level predictions and when used with a CLIP backbone,
provides a general improvement in zero-shot classification accuracy compared to
CLIP, across a suite of 12 image classification datasets.
