COMPUTER VISION

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

April 11, 2023

Abstract

We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP’s con- trastive 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 en- coders 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 im- provement in zero-shot classification accuracy compared to CLIP, across a suite of 12 image classification datasets.

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AUTHORS

Written by

Jishnu Mukhoti

Tsung-Yu Lin

Omid Poursaeed

Rui (VisionX) Wang

Ashish Shah

Philip Torr

Ser-Nam Lim

Publisher

CVPR

Research Topics

Computer Vision

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