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ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks.

, , , and . CoRR, (2023)

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Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation., , , , , , , and . NeurIPS, (2022)A Transformer-Based Object Detector with Coarse-Fine Crossing Representations., , , , , and . NeurIPS, (2022)Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification., , , , , and . ICCV, page 3641-3650. IEEE, (2019)Joint Supervision for Discriminative Feature Learning in Convolutional Neural Networks., , and . CCCV (2), volume 772 of Communications in Computer and Information Science, page 509-520. Springer, (2017)GhostNets on Heterogeneous Devices via Cheap Operations., , , , , , and . Int. J. Comput. Vis., 130 (4): 1050-1069 (2022)GhostNetV2: Enhance Cheap Operation with Long-Range Attention., , , , , and . CoRR, (2022)One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation., , , , , , and . CoRR, (2023)FastMIM: Expediting Masked Image Modeling Pre-training for Vision., , , , , and . CoRR, (2022)Masked Image Modeling with Local Multi-Scale Reconstruction., , , , , and . CVPR, page 2122-2131. IEEE, (2023)CMT: Convolutional Neural Networks Meet Vision Transformers., , , , , , and . CVPR, page 12165-12175. IEEE, (2022)