Synthetic Captions for Open-Vocabulary Zero-Shot Segmentation

• URL: http://arxiv.org/abs/2509.11840v1
• Date: Mon, 15 Sep 2025 12:26:47 GMT
• Title: Synthetic Captions for Open-Vocabulary Zero-Shot Segmentation
• Authors: Tim Lebailly, Vijay Veerabadran, Satwik Kottur, Karl Ridgeway, Michael Louis Iuzzolino,
• Abstract summary: We show how to densely align images with synthetic descriptions generated by generative vision-language models.<n>Our approach outperforms prior work on standard zero-shot open-vocabulary segmentation benchmarks/datasets.
• Score: 6.004292247258359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative vision-language models (VLMs) exhibit strong high-level image understanding but lack spatially dense alignment between vision and language modalities, as our findings indicate. Orthogonal to advancements in generative VLMs, another line of research has focused on representation learning for vision-language alignment, targeting zero-shot inference for dense tasks like segmentation. In this work, we bridge these two directions by densely aligning images with synthetic descriptions generated by VLMs. Synthetic captions are inexpensive, scalable, and easy to generate, making them an excellent source of high-level semantic understanding for dense alignment methods. Empirically, our approach outperforms prior work on standard zero-shot open-vocabulary segmentation benchmarks/datasets, while also being more data-efficient.

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