Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation

• URL: http://arxiv.org/abs/2501.04696v2
• Date: Sat, 08 Mar 2025 11:17:47 GMT
• Title: Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation
• Authors: Ulindu De Silva, Didula Samaraweera, Sasini Wanigathunga, Kavindu Kariyawasam, Kanchana Ranasinghe, Muzammal Naseer, Ranga Rodrigo,
• Abstract summary: Seg-TTO is a framework for zero-shot, open-vocabulary semantic segmentation.<n>We focus on segmentation-specific test-time optimization to address this gap.<n>Seg-TTO demonstrates clear performance improvements (up to 27% mIoU increase on some datasets) establishing new state-of-the-art.
• Score: 15.941958367737408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Seg-TTO, a novel framework for zero-shot, open-vocabulary semantic segmentation (OVSS), designed to excel in specialized domain tasks. While current open-vocabulary approaches show impressive performance on standard segmentation benchmarks under zero-shot settings, they fall short of supervised counterparts on highly domain-specific datasets. We focus on segmentation-specific test-time optimization to address this gap. Segmentation requires an understanding of multiple concepts within a single image while retaining the locality and spatial structure of representations. We propose a novel self-supervised objective adhering to these requirements and use it to align the model parameters with input images at test time. In the textual modality, we learn multiple embeddings for each category to capture diverse concepts within an image, while in the visual modality, we calculate pixel-level losses followed by embedding aggregation operations specific to preserving spatial structure. Our resulting framework termed Seg-TTO is a plug-and-play module. We integrate Seg-TTO with three state-of-the-art OVSS approaches and evaluate across 22 challenging OVSS tasks covering a range of specialized domains. Our Seg-TTO demonstrates clear performance improvements (up to 27% mIoU increase on some datasets) establishing new state-of-the-art. Our code and models will be released publicly.

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