Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation (2501.04696v2)

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.

• The paper introduces Seg-TTO, a framework applying segmentation-specific test-time optimization to improve zero-shot open-vocabulary semantic segmentation on challenging domain-specific tasks.
• Seg-TTO employs a self-supervised objective, aligns visual features with linguistic concepts using prompt tuning and visual feature selection, and leverages LLMs for automated attribute generation.
• Seg-TTO is plug-and-play, enhances state-of-the-art OVSS models, and establishes new benchmarks across 22 diverse segmentation tasks, enabling practical deployment in specialized fields like medical imaging and agriculture.

Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation

The research paper titled "Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation" presents Seg-TTO, an innovative framework aimed at advancing zero-shot open-vocabulary semantic segmentation (OVSS) to excel in domain-specific tasks. The framework addresses the gap in performance between supervised and open-vocabulary models on highly specialized datasets by leveraging segmentation-specific test-time optimization.

Recent developments in OVSS have harnessed contrastive vision-LLMs (VLMs) to produce models capable of segmenting a wide array of images without requiring direct supervision. Despite these advancements, a notable shortfall remains when applying these models to domain-specific tasks such as those in medical imaging or precision agriculture. These specialized use cases often lack comprehensive annotations, necessitating effective zero-shot approaches capable of addressing segmentation challenges that require understanding multiple concepts within a single image while maintaining spatial integrity.

The proposed Seg-TTO framework emphasizes a novel self-supervised objective that aligns model parameters with input images during test time. This method extends to the textual modality through the learning of multiple embeddings for each category, enabling the capture of diverse concepts within an image. On the visual side, the framework applies pixel-level losses followed by strategic embedding aggregation to preserve spatial structure, crucial for accurate segmentation.

Seg-TTO distinguishes itself through its plug-and-play applicability, enhancing existing OVSS models. When applied to three state-of-the-art OVSS models, Seg-TTO established new benchmarks across 22 challenging segmentation tasks across various domains, indicating its broad applicability and performance enhancement capabilities.

Key Contributions

• Test-Time Optimization for OVSS: The framework introduces a test-time optimization strategy for OVSS which enhances zero-shot performance in specialized domains by optimizing feature representations to better suit domain-specific data.
• Prompt and Feature Tuning: The framework employs a unique strategy for prompt tuning and visual feature selection to retain the spatial structure of segmentations and align with linguistic concepts inherent to the task.
• Automated Attribute Generation: By leveraging LLMs, Seg-TTO generates visually descriptive attributes to augment category representations, providing nuanced enhancements even for specialized vocabulary where conventional models underperform.

Evaluation and Results

Seg-TTO demonstrates improved performance across the MESS benchmark consisting of 22 datasets ranging from general to highly specialized domains, verifying its efficacy in both known and unfamiliar contexts. It outperformed existing baseline methods across most datasets, providing robust performance gains particularly in engineering, medical, and agricultural fields, where domain-specific terminology and visual language complexity pose significant challenges.

Implications and Future Directions

The introduction of Seg-TTO carries significant implications for the deployment of OVSS frameworks in practical, domain-specific applications where traditional supervised methods fall short due to high annotation costs or dataset rarity. This work not only broadens the practicality of OVSS models but also invites further exploration into the fusion of linguistic AI advancements with computer vision tasks, encouraging the intersectional growth of these technologies.

Future research directions may involve enhancing the LLM-based attribute generation process to enrich semantic understanding further and exploring adaptive techniques that reduce the computational overhead introduced by test-time optimizations. Moreover, the potential integration of community-driven datasets and annotations could propel the refinement of these segmentation models, underscoring Seg-TTO's foundational contribution to open-vocabulary semantic segmentation.