In this paper, we tackle the high computational overhead of transformers for lightweight image super-resolution. (SR). Motivated by the observations of self-attention's inter-layer repetition, we ...
Abstract: The state-of-the-art (SOTA) deep learning based time series models are inspired by convolutional neural networks (CNN), recurrent neural networks (RNN) or transformers which are successful ...
Abstract: Surgical phase recognition is a critical, yet challenging, problem in computer vision, with significant implications for automated surgical training, intraoperative assistance, and workflow ...
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