[Submitted on 18 Nov 2024]
View a PDF of the paper titled ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements, by M. Arda Ayd{i}n and 4 other authors
Abstract: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 this https URL.
Submission history
From: Mustafa Arda Aydın [view email]
[v1]
Mon, 18 Nov 2024 20:31:38 UTC (2,285 KB)
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