Petagna L, Antonelli A, Ganini C et al (2020) Pathophysiology of Crohn’s disease inflammation and recurrence. Biol Direct 15(1):23. https://doi.org/10.1186/s13062-020-00280-5
Article CAS PubMed PubMed Central Google Scholar
Marin-Santos D, Contreras-Fernandez J, Perez-Borrero I et al (2023) Automatic detection of Crohn disease in wireless capsule endoscopic images using a deep convolutional neural network. Appl Intell 53(10):12632-12646. https://doi.org/10.1007/s10489-022-04146-3
Ren S, He K, Girshick R, et al (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28
Zhu X, Su W, Lu L, et al (2020) Deformable detr: deformable transformers for end-to-end object detection. arXiv preprint arXiv:201004159
Lin TY, Dollar P, Girshick R, et al (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2117–2125
Liu S, Qi L, Qin H, et al (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8759–8768
Ma X, Dai X, Yang J, et al (2024) Efficient modulation for vision networks. In: The twelfth international conference on learning representations. https://openreview.net/forum?id=ip5LHJs6QX
Ma J, Chen B (2020) Dual refinement feature pyramid networks for object detection. arXiv preprint arXiv:2012.01733
Xu W, Wan Y (2024) Ela: Efficient local attention for deep convolutional neural networks. arXiv preprint arXiv:240301123
Liu S, Huang D, Wang Y (2019) Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:191109516
Ghiasi G, Lin TY, Le QV (2019) Nas-fpn: Learning scalable feature pyramid architecture for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7036–7045
Yang G, Lei J, Zhu Z, et al (2023) Afpn: asymptotic feature pyramid network for object detection. In: IEEE transactions on systems, man, and cybernetics, pp 2184–2189
Ouyang D, He S, Zhang G et al (2023) Efficient multi-scale attention module with cross-spatial learning. ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1–5
He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang G, Liu Z, Van Der Maaten L, et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Liu Z, Mao H, Wu CY, et al (2022) A convnet for the 2020s. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11976–11986
Guo MH, Lu CZ, Liu ZN et al (2023) Visual attention network. Comput Vis Media 9(4):733–752
Liu Z, Lin Y, Cao Y, et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022
Ding X, Zhang X, Han J et al (2022) Scaling up your kernels to 31x31: Revisiting large kernel design in CNNs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 11963–11975
Liu S, Chen T, Chen X, et al (2023) More convnets in the 2020s: scaling up kernels beyond 51 × 51 using sparsity. In: Proceedings of the International Conference on Learning Representations (ICLR)
Lou M, Zhou HY, Yang S, et al (2023) Transxnet: learning both global and local dynamics with a dual dynamic token mixer for visual recognition. arXiv preprint arXiv:231019380
Ma X, Dai X, Bai Y, et al (2024) Rewrite the stars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 5694–5703
Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13713–13722
Wang CY, Liao HYM, Wu YH, et al (2020) Cspnet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 390–391
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Chen J, Kao S, He H et al (2023) Run, don’t walk: Chasing higher flops for faster neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 12021–12031
Szegedy C, Ioffe S, Vanhoucke V, et al (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence
Lin TY, Maire M, Belongie S, et al (2014) Microsoft coco: common objects in context. In: Computer vision-ECCV 2014: 13th European conference, Zurich, Sept 6–12, 2014, Proceedings, Part V 13, Springer, pp 740–755
Paszke A, Gross S, Massa F, et al (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS)
Chen K, Wang J, Pang J, et al (2019) MMDetection: open mmlab detection toolbox and benchmark. arXiv preprint arXiv:190607155
Loshchilov I (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101
Deng J, Dong W, Socher R et al (2009) Imagenet: a large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255
He K, Zhang X, Ren S, et al (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
Sun P, Zhang R, Jiang Y, et al (2021) Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14454–14463
Peng Y, Zhang Y, Tu B et al (2022) Spatial-spectral transformer with cross-attention for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1–15
Zhang H, Chang H, Ma B, et al (2020) Dynamic R-CNN: Towards high quality object detection via dynamic training. In: Computer vision-ECCV 2020: 16th European conference, Glasgow, Aug 23–28, 2020, Proceedings, Part XV 16, Springer, pp 260–275
Liu S, Li F, Zhang H, et al (2022) Dab-detr: dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:220112329
Meng D, Chen X, Fan Z, et al (2021) Conditional detr for fast training convergence. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3651–3660
Zhang H, Li F, Liu S, et al (2022) Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:220303605
Zhang S, Wang X, Wang J, et al (2023) Dense distinct query for end-to-end object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7329–7338
Pang J, Chen K, Shi J, et al (2019) Libra R-CNN: towards balanced learning for object detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 821–830
Wang J, Chen K, Xu R, et al (2019) Carafe: content-aware reassembly of features. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3007–3016
Chen K, Cao Y, Loy CC, et al (2020) Feature pyramid grids. arXiv preprint arXiv:2004.03580
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