5 accepted papers
Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative erro
Whilst Convolutional Neural Network (CNN)-based object tracking methods can achieve promising results on traditional well-lit datasets, it is challenging to accurately locate targets in low-light images taken in nighttime scenes, even for state-of-the-art (SOTA) trackers. Existing solutions often di…
Despite recent advances in video-guided robotic imitation learning, many methods still rely on human experts to provide sparse rewards that indicate whether robots have successfully completed tasks. The challenge of enabling robots to autonomously evaluate whether their actions can complete complex,…
Deep convolutional neural networks have delivered remarkable aptitude in performing Synthetic Aperture Radar (SAR) image speckle removal tasks. Such approaches are nevertheless constrained in balancing speckle removal and preservation of spatial information, particularly with respect to strong speck…
Conventional approaches of robotic writing of Chinese character strokes often suffer from limited font generation methods, and thus the writing results often lack of diversity. This has seriously restricted the high quality writing ability of robots. This paper proposes a generative adversarial nets…