title: ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text,
    and Architectural Enhancements
summary: Recent advances in foundational Vision Language Models (VLMs) have reshaped
  the evaluation paradigm in computer vision tasks. These foundational models,
  especially CLIP, have accelerated research in open-vocabulary computer vision
  tasks, including Open-Vocabulary Semantic Segmentation (OVSS). Although the
  initial results are promising, the dense prediction capabilities of VLMs still
  require further improvement. In this study, we enhance the semantic
  segmentation performance of CLIP by introducing new modules and modifications:
  1) architectural changes in the last layer of ViT and the incorporation of
  attention maps from the middle layers with the last layer, 2) Image
  Engineering: applying data augmentations to enrich input image representations,
  and 3) using Large Language Models (LLMs) to generate definitions and synonyms
  for each class name to leverage CLIP's open-vocabulary capabilities. Our
  training-free method, ITACLIP, outperforms current state-of-the-art approaches
  on segmentation benchmarks such as COCO-Stuff, COCO-Object, Pascal Context, and
  Pascal VOC. Our code is available at https://github.com/m-arda-aydn/ITACLIP.
paper_authors._id: 673d97e8f48649bd99d56715
paper_authors.name: M. Arda Aydın
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-20T10:08:20.077Z
paper_authors._id: 63923c2a5ece53ea0b364c8a
paper_authors.avatarUrl: /avatars/d856933ecd8966ef72f1e67c6ec166b6.svg
paper_authors.fullname: Mustafa Arda Aydın
paper_authors.type: user
paper_authors.user: aydnarda
paper_authors._id: 673d97e8f48649bd99d56716
paper_authors.name: Efe Mert Çırpar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-20T13:33:17.654Z
paper_authors._id: 65f411590718933804abbf56
paper_authors.avatarUrl: /avatars/eb562172c0587c89288cfc5f32205a41.svg
paper_authors.fullname: Efe Mert Çırpar
paper_authors.type: user
paper_authors.user: efemcirpar
paper_authors._id: 673d97e8f48649bd99d56717
paper_authors.name: Elvin Abdinli
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-21T08:09:47.221Z
paper_authors._id: 673e20b9635a89280bfd901a
paper_authors.avatarUrl: /avatars/95cb42c7bc30a9c23eadd51bf961454d.svg
paper_authors.fullname: Elvin Abdinli
paper_authors.type: user
paper_authors.user: elvinabdinli
paper_authors._id: 673d97e8f48649bd99d56718
paper_authors.name: Gozde Unal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-20T13:33:26.419Z
paper_authors._id: 644b88b00fbe4830f192583f
paper_authors.avatarUrl: /avatars/0e1cbab70d9b3ad4340985ee3087a8a7.svg
paper_authors.fullname: Gozde Unal
paper_authors.type: user
paper_authors.user: gozy
paper_authors._id: 673d97e8f48649bd99d56719
paper_authors.name: Yusuf H. Sahin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-20T13:33:31.767Z
paper_authors._id: 6454104b9dcddd329ae73fc6
paper_authors.avatarUrl: /avatars/996ee479de8efa7c1deafe8de634e94b.svg
paper_authors.fullname: Yusuf H Sahin
paper_authors.type: user
paper_authors.user: sahinyu
paper_title: ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text,
    and Architectural Enhancements
paper_summary: Recent advances in foundational Vision Language Models (VLMs) have reshaped
  the evaluation paradigm in computer vision tasks. These foundational models,
  especially CLIP, have accelerated research in open-vocabulary computer vision
  tasks, including Open-Vocabulary Semantic Segmentation (OVSS). Although the
  initial results are promising, the dense prediction capabilities of VLMs still
  require further improvement. In this study, we enhance the semantic
  segmentation performance of CLIP by introducing new modules and modifications:
  1) architectural changes in the last layer of ViT and the incorporation of
  attention maps from the middle layers with the last layer, 2) Image
  Engineering: applying data augmentations to enrich input image representations,
  and 3) using Large Language Models (LLMs) to generate definitions and synonyms
  for each class name to leverage CLIP's open-vocabulary capabilities. Our
  training-free method, ITACLIP, outperforms current state-of-the-art approaches
  on segmentation benchmarks such as COCO-Stuff, COCO-Object, Pascal Context, and
  Pascal VOC. Our code is available at https://github.com/m-arda-aydn/ITACLIP.
paper_ai_summary: ITACLIP enhances CLIP's semantic segmentation through architectural modifications, data augmentation, and language model-generated definitions, outperforming existing benchmarks.
paper_ai_keywords: Vision Language Models CLIP Open-Vocabulary Semantic Segmentation ViT attention maps Large Language Models ITACLIP COCO-Stuff COCO-Object Pascal Context Pascal VOC