Owing to the mutual orthogonality among different orbital angular momentums (OAM), OAM mode has been considered as a promising candidate for improving channel capacity and spectrum efficiency. Howe…
In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the…
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynamic networks can ad…
Wearable sensing devices, which can find tremendous applications in healthcare and automation, are attracting great attention in recent years. Herein, we report a flexible wearable optical sensor (…
A major gap between few-shot and many-shot learning is the data distribution empirically oserved by the model during training. In few-shot learning, the learned model can easily become over-fitted …
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable result…
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA tar…
The exponential growth in data-driven applications like artificial intelligence and metaverse poses an increasing demand for integrated optical interconnects with efficient and cost-effective high-…
Evolutionary sequential transfer optimization is a paradigm that leverages search experience from solved source optimization tasks to accelerate the evolutionary search of a target task. Even thoug…
Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is …