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目标检测 |
FasterRCNN |
Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28. |
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| [2] |
目标检测 |
Yolov3 |
Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018. |
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| [3] |
目标检测 |
Yolov4 |
Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020. |
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| [4] |
目标检测 |
CornerNet |
Law H, Deng J. Cornernet: Detecting objects as paired keypoints[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 734-750. |
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| [5] |
目标检测 |
CenterNet |
Zhao Z Q, Zheng P, Xu S, et al. Object detection with deep learning: A review[J]. IEEE transactions on neural networks and learning systems, 2019, 30(11): 3212-3232. |
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| [6] |
目标检测 |
DETR |
Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers[C]//European conference on computer vision. Cham: Springer International Publishing, 2020: 213-229. |
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目标跟踪 |
ARTrack |
Wei X, Bai Y, Zheng Y, et al. Autoregressive visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 9697-9706. |
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| [8] |
目标跟踪 |
DropMAE |
Wu Q, Yang T, Liu Z, et al. Dropmae: Masked autoencoders with spatial-attention dropout for tracking tasks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 14561-14571. |
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目标跟踪 |
QCT |
Zhu W, Xu L, Meng J. Consistency-based self-supervised visual tracking by using query-communication transformer[J]. Knowledge-Based Systems, 2023, 278: 110849. |
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目标跟踪 |
RTS |
Paul M, Danelljan M, Mayer C, et al. Robust visual tracking by segmentation[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 571-588. |
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目标跟踪 |
ConTACT |
Choi J, Baik S, Choi M, et al. Visual tracking by adaptive continual meta-learning[J]. IEEE Access, 2022, 10: 9022-9035. |
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图像分割 |
DetectoRS |
Qiao S, Chen L C, Yuille A. Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 10213-10224. |
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图像分割 |
PolarMask |
Xie E, Sun P, Song X, et al. Polarmask: Single shot instance segmentation with polar representation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 12193-12202. |
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图像分割 |
Segment Anything |
Kirillov A, Mintun E, Ravi N, et al. Segment anything[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 4015-4026. |
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人体动作识别 |
Temporal Templates |
Bobick A F, Davis J W. The recognition of human movement using temporal templates[J]. IEEE Transactions on pattern analysis and machine intelligence, 2001, 23(3): 257-267. |
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循环神经网络 |
RNN |
Schmidt R M. Recurrent neural networks (rnns): A gentle introduction and overview[J]. arXiv preprint arXiv:1912.05911, 2019. |
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循环神经网络 |
LSTM |
Graves A, Graves A. Long short-term memory[J]. Supervised sequence labelling with recurrent neural networks, 2012: 37-45. |
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循环神经网络 |
GRU |
Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv:1412.3555, 2014. |
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循环神经网络 |
CompositeLSTM |
Srivastava N, Mansimov E, Salakhudinov R. Unsupervised learning of video representations using lstms[C]//International conference on machine learning. PMLR, 2015: 843-852. |
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| [20] |
循环神经网络 |
ConvLSTM |
Shi X, Chen Z, Wang H, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting[J]. Advances in neural information processing systems, 2015, 28. |
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循环神经网络 |
LRCN |
Donahue J, Anne Hendricks L, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2625-2634. |
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| [22] |
三维卷积特征提取 |
C3D |
Tran D, Bourdev L, Fergus R, et al. Learning spatiotemporal features with 3d convolutional networks[C]//Proceedings of the IEEE international conference on computer vision. 2015: 4489-4497. |
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三维卷积特征提取 |
I3D |
Peng Y, Lee J, Watanabe S. I3D: Transformer architectures with input-dependent dynamic depth for speech recognition[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5. |
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图神经网络 |
GNN |
Gori M, Monfardini G, Scarselli F. A new model for learning in graph domains[C]//Proceedings. 2005 IEEE international joint conference on neural networks, 2005. IEEE, 2005, 2: 729-734. |
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图卷积网络 |
ConvGNN |
Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs[J]. arXiv preprint arXiv:1312.6203, 2013. |
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图卷积网络 |
ConvGNN |
Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data[J]. arXiv preprint arXiv:1506.05163, 2015. |
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图卷积网络 |
Diffusion ConvGNN |
Atwood J, Towsley D. Diffusion-convolutional neural networks[J]. Advances in neural information processing systems, 2016, 29. |
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人体动作识别 |
ST-GCN |
Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1). |
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人体动作识别 |
MS-G3D |
Liu Z, Zhang H, Chen Z, et al. Disentangling and unifying graph convolutions for skeleton-based action recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 143-152. |
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人体动作识别 |
CTR-GCN |
Chen Y, Zhang Z, Yuan C, et al. Channel-wise topology refinement graph convolution for skeleton-based action recognition[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 13359-13368. |
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数据集 |
NTU RGB+D |
Shahroudy A, Liu J, Ng T T, et al. Ntu rgb+ d: A large scale dataset for 3d human activity analysis[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1010-1019. |
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数据集 |
NTU RGB+D 120 |
Liu J, Shahroudy A, Perez M, et al. Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 42(10): 2684-2701. |
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| [33] |
数据集 |
Kinetics |
Kay W, Carreira J, Simonyan K, et al. The kinetics human action video dataset[J]. arXiv preprint arXiv:1705.06950, 2017. |
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| [34] |
图神经网络 |
GNN |
Sperduti A, Starita A. Supervised neural networks for the classification of structures[J]. IEEE transactions on neural networks, 1997, 8(3): 714-735. |
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| [35] |
卷积神经网络 |
CNN综述 |
Hadji I, Wildes R P. What do we understand about convolutional networks?[J]. arXiv preprint arXiv:1803.08834, 2018. |
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图像特征提取 |
LBP |
Hadid A. The local binary pattern approach and its applications to face analysis[C]//2008 First Workshops on Image Processing Theory, Tools and Applications. IEEE, 2008: 1-9. |
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卷积神经网络 |
CNN |
D. H. Hubel and T. N. Wiesel.Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex.The Journal of Physiology, 160:106–154, 1962. |
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目标检测 |
Yolov9 |
Wang C Y, Yeh I H, Liao H Y M. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information[J]. arXiv preprint arXiv:2402.13616, 2024. |
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数据结构 |
数据结构与算法 |
梁海英,王凤领.数据结构：C语言版[M].北京:清华大学出版社,2017. |
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人体动作识别 |
2s-AGCN |
Shi L, Zhang Y, Cheng J, et al. Two-stream adaptive graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 12026-12035. |
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| [41] |
梯度下降策略 |
SGD |
Robbins H, Monro S. A stochastic approximation method[J]. The annals of mathematical statistics, 1951: 400-407. |
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梯度下降策略 |
Adam |
Diederik P K. Adam: A method for stochastic optimization[J]. (No Title), 2014. |
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| [43] |
梯度下降策略 |
Dropout |
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The journal of machine learning research, 2014, 15(1): 1929-1958. |
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注意力机制 |
Attention |
Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: A survey[J]. Computational visual media, 2022, 8(3): 331-368. |
|