Abstract: This article introduces a simple framework for depth-augmented contrastive learning (SimDCL), a novel approach to enhance endoscopic image classification by incorporating depth information.
Abstract: Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated training ...
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