% Key papers for Efficient CNN scaling and dynamic inference (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2016_rethinking_11815,
  title = {{Rethinking the Inception Architecture for Computer Vision}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2018_squeeze_13608,
  title = {{Squeeze-and-Excitation Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html},
}

@inproceedings{cvpr2017_xception_12763,
  title = {{Xception: Deep Learning With Depthwise Separable Convolutions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_aggregated_12052,
  title = {{Aggregated Residual Transformations for Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_shufflenet_13585,
  title = {{ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html},
}

@inproceedings{cvpr2016_accurate_11382,
  title = {{Accurate Image Super-Resolution Using Very Deep Convolutional Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{icml2019_efficientnet_37332,
  title = {{EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3661},
}

@inproceedings{cvpr2020_ghostnet_15605,
  title = {{GhostNet: More Features From Cheap Operations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2017_residual_12582,
  title = {{Residual Attention Network for Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_pwc_13454,
  title = {{PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Sun_PWC-Net_CNNs_for_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_repvgg_17737,
  title = {{RepVGG: Making VGG-Style ConvNets Great Again}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2017_sca_12594,
  title = {{SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2019_second_14782,
  title = {{Second-Order Attention Network for Single Image Super-Resolution}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2023_convnext_20656,
  title = {{ConvNeXt V2: Co-Designing and Scaling ConvNets With Masked Autoencoders}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22892},
}

@inproceedings{cvpr2018_deep_12969,
  title = {{Deep Layer Aggregation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Deep_Layer_Aggregation_CVPR_2018_paper.html},
}

@inproceedings{cvpr2019_do_14104,
  title = {{Do Better ImageNet Models Transfer Better?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_deeper_14059,
  title = {{Deeper and Wider Siamese Networks for Real-Time Visual Tracking}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_fast_13103,
  title = {{Fast and Accurate Single Image Super-Resolution via Information Distillation Network}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Hui_Fast_and_Accurate_CVPR_2018_paper.html},
}

@inproceedings{cvpr2023_internimage_21272,
  title = {{InternImage: Exploring Large-Scale Vision Foundation Models With Deformable Convolutions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22200},
}

@inproceedings{cvpr2018_condensenet_12911,
  title = {{CondenseNet: An Efficient DenseNet Using Learned Group Convolutions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Huang_CondenseNet_An_Efficient_CVPR_2018_paper.html},
}

@inproceedings{cvpr2024_inceptionnext_23793,
  title = {{InceptionNeXt: When Inception Meets ConvNeXt}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29336},
}

@inproceedings{cvpr2025_are_25508,
  title = {{Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34936},
}

@inproceedings{aaai2024_another_5194,
  title = {{Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28791},
}

@inproceedings{cvpr2024_pick_24467,
  title = {{Pick-or-Mix: Dynamic Channel Sampling for ConvNets}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31658},
}

@inproceedings{neurips2024_ascan_74069,
  title = {{AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93461},
}

@inproceedings{neurips2024_convolutions_72982,
  title = {{Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94434},
}

@inproceedings{neurips2025_mind_76664,
  title = {{Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=LrBWGwVfCA},
}

@inproceedings{icml2026_convolutional_54963,
  title = {{Convolutional Learnable-Group Weightless Neural Network}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=uMUGXv4881},
}

@inproceedings{cvpr2026_qucnet_96591,
  title = {{QuCNet: Quantum Deep Learning Driven Multi-Circuit Network for Remote Sensing Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Komal_QuCNet_Quantum_Deep_Learning_Driven_Multi-Circuit_Network_for_Remote_Sensing_CVPR_2026_paper.html},
}