1 |
HERATH S, HARANDI M, PORIKLI F. Going deeper into action recognition: a survey. Image and Vision Computing, 2017, 60, 4- 21.
doi: 10.1016/j.imavis.2017.01.010
|
2 |
吴建超, 王利民, 武港山. 视频群体行为识别综述. 软件学报, 2023, 34(2): 964- 984.
|
|
WU J C, WANG L M, WU G S. Group activity recognition in videos: a survey. Journal of Software, 2023, 34(2): 964- 984.
|
3 |
鹿天然, 于凤芹, 陈莹. 有效视频帧时间序池化的人体行为识别算法. 计算机工程, 2018, 44(12): 271-275, 287.
doi: 10.19678/j.issn.1000-3428.0049178
|
|
LU T R, YU F Q, CHEN Y. Human action recognition algorithm with temporal rank pooling of valid video frames. Computer Engineering, 2018, 44(12): 271-275, 287.
doi: 10.19678/j.issn.1000-3428.0049178
|
4 |
孙彦玺, 赵婉婉, 武东辉, 等. 基于卷积长短时记忆网络的人体行为识别研究. 计算机工程, 2021, 47(10): 260- 268.
doi: 10.19678/j.issn.1000-3428.0060938
|
|
SUN Y X, ZHAO W W, WU D H, et al. Research of human activity recognition based on convolutional long short-term memory network. Computer Engineering, 2021, 47(10): 260- 268.
doi: 10.19678/j.issn.1000-3428.0060938
|
5 |
PIERGIOVANNI A J, ANGELOVA A, RYOO M S. Tiny video networks. Applied AI Letters, 2022, 3(1): 38.
doi: 10.1002/ail2.38
|
6 |
WANG L M, XIONG Y J, WANG Z, et al. Temporal segment networks: towards good practices for deep action recognition[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 20-36.
|
7 |
ZHI Y A, TONG Z, WANG L M, et al. MGSampler: an explainable sampling strategy for video action recognition[C]//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2021: 1513-1522.
|
8 |
FAN H H, XU Z W, ZHU L C, et al. Watching a small portion could be as good as watching all: towards efficient video classification[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. New York, USA: ACM Press, 2018: 705-711.
|
9 |
CUBUK E D, ZOPH B, SHLENS J, et al. RandAugment: practical automated data augmentation with a reduced search space[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2020: 18613-18624.
|
10 |
YUN S, HAN D, CHUN S, et al. Cutmix: regularization strategy to train strong classifiers with localizable features[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 6023-6032.
|
11 |
YOO J, AHN N, SOHN K A. Rethinking data augmentation for image super-resolution: a comprehensive analysis and a new strategy[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 8375-8384.
|
12 |
MENG Y, LIN C C, PANDA R, et al. AR-Net: adaptive frame resolution for efficient action recognition[C]// Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 86-104.
|
13 |
KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2014: 1725-1732.
|
14 |
ZHU X Q, XU C, HUI L W, et al. Approximated bilinear modules for temporal modeling[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 3494-3503.
|
15 |
WALAWALKAR D, SHEN Z, LIU Z, et al. Attentive Cutmix: an enhanced data augmentation approach for deep learning based image classification[EB/OL]. [2023-12-11]. https://arxiv.org/abs/2003.13048.
|
16 |
CUBUK E D, ZOPH B, MANE D, et al. AutoAugment: learning augmentation strategies from data[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2019: 113-123.
|
17 |
LIM S, KIM I, KIM T, et al. Fast AutoAugment[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2019: 6665-6675.
|
18 |
GOYAL R, KAHOU S E, MICHALSKI V, et al. The "Something Something" video database for learning and evaluating visual common sense[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2017: 5842-5850.
|
19 |
LIN J, GAN C A, HAN S. TSM: temporal shift module for efficient video understanding[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 7083-7093.
|
20 |
LI Y, JI B, SHI X T, et al. TEA: temporal excitation and aggregation for action recognition[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 909-918.
|
21 |
LUO C X, YUILLE A. Grouped spatial-temporal aggregation for efficient action recognition[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 5512-5521.
|
22 |
LIU Z, LUO D, WANG Y, et al. TEINet: towards an efficient architecture for video recognition[C]//Proceedings of AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2020: 11669-11676.
|
23 |
SUDHAKARAN S, ESCALERA S, LANZ O. Gate-shift networks for video action recognition[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 1102-1111.
|
24 |
WENG J W, LUO D H, WANG Y B, et al. Temporal distinct representation learning for action recognition[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 363-378.
|
25 |
KWON H, KIM M, KWAK S, et al. MotionSqueeze: neural motion feature learning for video understanding[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 345-362.
|
26 |
WU W H, HE D L, LIN T W, et al. MVFNet: multi-view fusion network for efficient video recognition[C]//Proceedings of AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2021: 2943-2951.
|
27 |
|
28 |
WANG L M, TONG Z, JI B, et al. TDN: temporal difference networks for efficient action recognition[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2021: 1895-1904.
|
29 |
CHEN X, HAN Y, WANG X, et al. Action keypoint network for efficient video recognition. IEEE Transactions on Image Processing, 2022, 31, 4980- 4993.
doi: 10.1109/TIP.2022.3191461
|