# ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Source: [ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:30:26.676Z Authors: M. Arda Ayd{\\i}n, Efe Mert \\c{C}{\\i}rpar, Elvin Abdinli, Gozde Unal, Yusuf H. Sahin Published: 2024-11-18T20:31:38.000Z ## 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 https://github.com/m-arda-aydn/ITACLIP. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()