Titlebook: Information and Communication Technology and the Teacher of the Future; IFIP TC3 / WG3.1 & W Carolyn Dowling,Kwok-Wing Lai Book 2003 IFIP I

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Ian Webb,Toni Downesghtweight ConvNets across a variety of deep learning architectures, including ViTs, ConvNets, and hybrid transformers, without any re-training. Moreover, the simple early-stage one-step patch pruning with PaPr enhances existing patch reduction methods. Through extensive testing on diverse architectu

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Raymond Morea bio.echanically .ccurate .eural .nverse .ematics solver (MANIKIN) for full-body motion tracking. MANIKIN is based on swivel angle prediction and perfectly matches input poses while avoiding ground penetration. We evaluate MANIKIN in extensive experiments on motion capture datasets and demonstrate

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Sigrid Schubertnnotated only for the classes of their original step, we devise balanced anti-misguidance losses, which combat the impact of incomplete annotations without incurring classification bias. Building upon these innovations, we present a new method named Balanced Continual Panoptic Segmentation (BalConpa

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Margaret Coxing baseline, SimCLR, and accelerates convergence. Comparison against four recent contrastive and masked autoencoding-based methods for remote sensing also highlight the efficacy of our approach. Furthermore, our framework enables multimodal classification with metadata to improve the performance of

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