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— In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through comprehensive benchmark analyses on cave systems in USA, Mexico, and Spain locations, we demonstrate that robust deep visual models can be developed based on CaveSeg for fast semantic scene parsing of underwater cave environments. In particular, we formulate a novel transformer-based model that is computationally light and offers near real-time execution in addition to achieving state-of-the-art performance. Finally, we explore the design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves. The proposed model and benchmark dataset open up promising opportunities for future research in autonomous underwater cave exploration and mapping.
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Multimodal Contextualized Semantic Parsing from Speech
his paper introduces Semantic Parsing in Contextual Environments (SPICE), a task aimed at improving artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. Unlike traditional semantic parsing, SPICE provides a structured and interpretable framework for dynamically updating an agent’s knowledge with new information, reflecting the complexity of human communication. To support this task, the authors develop the VG-SPICE dataset, which challenges models to construct visual scene graphs from spoken conversational exchanges, emphasizing the integration of speech and visual data. They also present the Audio-Vision Dialogue Scene Parser (AViD-SP), a model specifically designed for VG-SPICE. Both the dataset and model are released publicly, with the goal of advancing multimodal information processing and integration.
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- Award ID(s):
- 2505865
- PAR ID:
- 10631956
- Publisher / Repository:
- https://doi.org/10.48550/arXiv.2406.06438
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
