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All Tokens Matter: Token Labeling for Training Better Vision Transformers.

, , , , , , , and . NeurIPS, page 18590-18602. (2021)

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Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning., , , , and . CoRR, (2022)Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning., , , , , , , and . CVPR, page 16701-16710. IEEE, (2022)Second-Order Polynomial Equation-Based Block Adjustment for Orthorectification of DISP Imagery., , , , and . Remote. Sens., 8 (8): 680 (2016)Refiner: Refining Self-attention for Vision Transformers., , , , , , , , and . CoRR, (2021)Envision3D: One Image to 3D with Anchor Views Interpolation., , , , , , , , and . CoRR, (2024)Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation., , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (3): 1415-1428 (2022)Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet., , , , , , , , and . ICCV, page 538-547. IEEE, (2021)Predictive Flux-linkage Control for Novel Dual-Permanent-Magnet-Excited Machines., , , and . ICIA, page 173-177. IEEE, (2018)Quantitative Analysis of Back-EMF of a Dual-Permanent-Magnet-Excited Machine: Alert to Flux Density Harmonics Which Make a Negative Contribution to Back-EMF., , and . IEEE Access, (2021)Continual Learning via Bit-Level Information Preserving., , , and . CVPR, page 16674-16683. Computer Vision Foundation / IEEE, (2021)