7 accepted papers
Foundation models for intracranial neural recordings aim to learn generalizable representations from large-scale unlabeled data. However, existing approaches rely on suboptimal tokenization schemes -- treating individual electrode channels as independent tokens or aggregating them into a single brai…
Image fusion integrates complementary information from different modalities to generate high-quality fused images, thereby enhancing downstream tasks such as object detection and semantic segmentation. Unlike task-specific techniques that primarily focus on consolidating inter-modal information, gen
Understanding the dynamic physical world, characterized by its evolving 3D structure, real-world motion, and semantic content with textual descriptions, is crucial for human-agent interaction and enables embodied agents to perceive and act within real environments with human‑like capabilities. Howev…
Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicabil…
Invasive brain-computer interfaces with Electrocorticography (ECoG) have shown promise for high-performance speech decoding in medical applications, but less damaging methods like intracranial stereo-electroencephalography (sEEG) remain underexplored. With rapid advances in representation learning,…
To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objective evolutionary optimization. Firstly, path solutions are generated by employing spline interpolation on the digital elevation mod
We propose a new method for the propagation of semantic labels in RGB-D video of indoor scenes given a set of ground truth keyframes. Manual labeling of all pixels in every frame of a video sequence is labor intensive and costly, yet required for training and testing of semantic segmentation methods…