Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning
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...
| Главные авторы: | , , , , , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
IEEE
2023
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| _version_ | 1826311559665680384 |
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| author | Mukhoti, J Lin, T-Y Poursaeed, O Wang, R Shah, A Torr, PHS Lim, S-N |
| author_facet | Mukhoti, J Lin, T-Y Poursaeed, O Wang, R Shah, A Torr, PHS Lim, S-N |
| author_sort | Mukhoti, J |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T08:11:35Z |
| format | Conference item |
| id | oxford-uuid:c359c40a-e93f-4303-b3c0-eb50e1c69a39 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:11:35Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:c359c40a-e93f-4303-b3c0-eb50e1c69a392023-11-24T09:30:52ZOpen vocabulary semantic segmentation with Patch Aligned Contrastive LearningConference itemhttp://purl.org/coar/resource_type/c_5794uuid:c359c40a-e93f-4303-b3c0-eb50e1c69a39EnglishSymplectic ElementsIEEE2023Mukhoti, JLin, T-YPoursaeed, OWang, RShah, ATorr, PHSLim, S-NWe 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. |
| spellingShingle | Mukhoti, J Lin, T-Y Poursaeed, O Wang, R Shah, A Torr, PHS Lim, S-N Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title | Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title_full | Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title_fullStr | Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title_full_unstemmed | Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title_short | Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning |
| title_sort | open vocabulary semantic segmentation with patch aligned contrastive learning |
| work_keys_str_mv | AT mukhotij openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT linty openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT poursaeedo openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT wangr openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT shaha openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT torrphs openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning AT limsn openvocabularysemanticsegmentationwithpatchalignedcontrastivelearning |