4 accepted papers
Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary pro…
In ultra-high resolution image segmentation task for robotic platforms like UAVs and autonomous vehicles, existing paradigms process a downsampled input image through a deep network and the original high-resolution image through a shallow network, then fusing their features for final segmentation. A
Robots, constrained by limited onboard computing resources, often encounter situations wherein high-resolution and high-bit-rate videos captured by their cameras necessitate compression before further analysis. In this paper, we propose a novel video semantic segmentation paradigm for compressed vid…
Ultra-high resolution image segmentation poses a formidable challenge for UAVs with limited computation resources. Moreover, with multiple deployed tasks (e.g., mapping, localization, and decision making), the demand for a memory efficient model becomes more urgent. This letter delves into the intri