3 accepted papers
In this work, we present CollabVLA, a self-reflective vision-language-action framework that transforms a standard visuomotor policy into a collaborative assistant. CollabVLA tackles key limitations of prior VLAs, including domain overfitting, non-interpretable reasoning, and the high latency of auxi…
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however in the context of deep learning existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics but also manually generate low-resolutio…
Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges. A pivotal aspect of ensuring data accuracy and consistency across such systems involves the calibration of LiDAR units a…