Open-vocabulary semantic segmentation (OVSS) entails assigning semantic labels to each pixel in an image using textual descriptions, typically leveraging world models such as CLIP. To enhance out-of-domain generalization, we propose Cost Aggregation with Optimal Transport (OV-COAST) for open-vocabulary semantic segmentation. To align visual-language features within the framework of optimal transport theory, we employ cost volume to construct a cost matrix, which quantifies the distance between two distributions. Our approach adopts a two-stage optimization strategy: in the first stage, the optimal transport problem is solved using cost volume via Sinkhorn distance to obtain an alignment solution; in the second stage, this solution is used to guide the training of the CAT-Seg model. We evaluate state-of-the-art OVSS models on the MESS benchmark, where our approach notably improves the performance of the cost-aggregation model CAT-Seg with ViT-B backbone, achieving superior results, surpassing CAT-Seg by 1.72 % and SAN-B by 4.9 % mIoU. The code is available at https://github.com/adityagandhamal/OV-COAST/}{https://github.com/adityagandhamal/OV-COAST/ .
@article{arxiv.2506.03706,
title = {OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation},
author = {Aditya Gandhamal and Aniruddh Sikdar and Suresh Sundaram},
journal= {arXiv preprint arXiv:2506.03706},
year = {2025}
}
Comments
Accepted at CVPR 2025 Workshop on Transformers for Vision (Non-archival track)