AetherVision-Bench: An Open-Vocabulary RGB-Infrared Benchmark for Multi-Angle Segmentation across Aerial and Ground Perspectives

• URL: http://arxiv.org/abs/2506.03709v1
• Date: Wed, 04 Jun 2025 08:41:19 GMT
• Title: AetherVision-Bench: An Open-Vocabulary RGB-Infrared Benchmark for Multi-Angle Segmentation across Aerial and Ground Perspectives
• Authors: Aniruddh Sikdar, Aditya Gandhamal, Suresh Sundaram,
• Abstract summary: Embodied AI systems are transforming autonomous navigation for ground vehicles and drones by enhancing their perception abilities.<n>We present AetherVision-Bench, a benchmark for multi-angle segmentation across aerial, and ground perspectives.<n>We assess state-of-the-art OVSS models on the proposed benchmark and investigate the key factors that impact the performance of zero-shot transfer models.
• Score: 2.0293118701268154
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Open-vocabulary semantic segmentation (OVSS) involves assigning labels to each pixel in an image based on textual descriptions, leveraging world models like CLIP. However, they encounter significant challenges in cross-domain generalization, hindering their practical efficacy in real-world applications. Embodied AI systems are transforming autonomous navigation for ground vehicles and drones by enhancing their perception abilities, and in this study, we present AetherVision-Bench, a benchmark for multi-angle segmentation across aerial, and ground perspectives, which facilitates an extensive evaluation of performance across different viewing angles and sensor modalities. We assess state-of-the-art OVSS models on the proposed benchmark and investigate the key factors that impact the performance of zero-shot transfer models. Our work pioneers the creation of a robustness benchmark, offering valuable insights and establishing a foundation for future research.

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