Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability ...

Most learning-based speech enhancement pipelines depend on paired clean–noisy recordings, which are expensive or impossible to collect at scale in real-world conditions. Unsupervised routes like ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

First of all, I'd like to commend the authors on the excellent work presented in SSS! I have a quick question regarding the model architecture, specifically related to the frozen image encoder and ...

Abstract: In unsupervised medical image registration, encoder-decoder architectures are widely used to predict dense, full-resolution displacement fields from paired images. Despite their popularity, ...

Thanks for sharing this clean codebase to your cool paper and congrats to achieving sota. I got a general question which I did not understand from the paper and this codebase about the architecture ...

Abstract: Image captioning is a multi-modal problem linking computer vision and natural language processing, which combines image analysis and text generation challenges. In the literature, most of ...

I wrote a bit of VHDL for generating inc/dec pulses from quadrature rotary encoders like the CTS Electrocomponents ones I bought from Digikey for a project I worked on recently. This debounce circuit ...