Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (16): 94-104.DOI: 10.3778/j.issn.1002-8331.2311-0452
• Pattern Recognition and Artificial Intelligence • Previous Articles Next Articles
XIANG Jun, ZHANG Jincheng, JIANG Xiaoping, HOU Jianhua
Online:
2024-08-15
Published:
2024-08-15
项俊,张金城,江小平,侯建华
XIANG Jun, ZHANG Jincheng, JIANG Xiaoping, HOU Jianhua. Cross-Attention Fusion Learning of Transformer-CNN Features for Person Re-Identification[J]. Computer Engineering and Applications, 2024, 60(16): 94-104.
项俊, 张金城, 江小平, 侯建华. Transformer-CNN特征跨注意力融合学习的行人重识别[J]. 计算机工程与应用, 2024, 60(16): 94-104.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2311-0452
[1] LIAO S, HU Y, ZHU X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 2197-2206. [2] ZHENG L, YANG Y, HAUPTMANN A G. Person re-identification: past, present and future[J]. arXiv:1610.02984, 2016. [3] ZHENG L, ZHANG H, SUN S, et al. Person re-identification in the wild[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 1367-1376. [4] SUN Y, ZHENG L, YANG Y, et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the 15th European Conference on Computer Vision, Munich, 2018: 480-496. [5] WANG G, YUAN Y, CHEN X, et al. Learning discriminative features with multiple granularities for person re-identification[C]//Proceedings of the 26th ACM International Conference on Multimedia. New York: ACM, 2018: 274-282. [6] LUO H, JIANG W, ZHANG X, et al. AlignedreID++: dynamically matching local information for person re-identification[J]. Pattern Recognition, 2019, 94: 53-61. [7] SUN Y, CHENG C, ZHANG Y, et al. Circle loss: a unified perspective of pair similarity optimization[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 6398-6407. [8] 杨永胜, 邓淼磊, 张德贤. 基于IBN-Net和通道注意力的行人重识别方法[J]. 计算机工程与应用, 2023, 59(17): 143-151. YANG Y S, DENG M L, ZHANG D X. Person re-identification method based on IBN-Net and channel attention[J]. Computer Engineering and Application, 2023, 59(17): 143-151. [9] 陈璠, 彭力. 异构分支关联特征融合的行人重识别[J]. 计算机科学与探索, 2022, 16(11): 2609-2618. CHEN F, PENG L. Person re-identification based on heterogeneous branch correlative features fusion[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(11): 2609-2618. [10] 钱亚萍, 王凤随, 熊磊. 基于局部细化多分支与全局特征共享的无监督行人重识别方法[J]. 电子测量与仪器学报, 2023, 37(1): 106-115. QIAN Y P, WANG F S, XIONG L. Unsupervised person re-identification method based on local refinement multi-branch and global feature sharing[J]. Journal of Electronic Measurement and Instrumentation, 2023, 37(1): 106-115. [11] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30, 2017. [12] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[J]. arXiv:2010.11929, 2020. [13] LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, Montreal, 2021: 10012-10022. [14] HE S, LUO H, WANG P, et al. TransreID: transformer-based object re-identification[C]//Proceedings of the 2021 IEEE International Conference on Computer Vision, Montreal, 2021: 15013-15022. [15] LEE K, JANG I S, KIM K J, et al. REET: region-enhanced transformer for person re-identification[C]//Proceedings of the 2022 IEEE International Conference on Advanced Video and Signal Based Surveillance, Madrid, 2022: 1-8. [16] PENG Z, HUANG W, GU S, et al. Conformer: local features coupling global representations for visual recognition[C]//Proceedings of the 2021 IEEE International Conference on Computer Vision, Montreal, 2021: 367-376. [17] LI H, YE M, WANG C, et al. Pyramidal transformer with Conv-Patchify for person re-identification[C]//Proceedings of the 30th ACM International Conference on Multimedia. New York: ACM, 2022: 7317-7326. [18] XIE C X, XIA C Q, MA M C, et al. Pyramid grafting network for one-stage high resolution saliency detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 2022: 11717-11726. [19] 王静, 李沛橦, 赵容锋, 等. 融合卷积注意力Transformer架构的行人重识别方法[J]. 北京航空航天大学学报, 2024, 50(2): 466-476. WANG J, LI P T, ZHAO R F, et al. A person re-identification method for fusing convolutional attention and transformer architecture[J]. Journal of Beihang University, 2024, 50(2): 466-476. [20] ZHANG G, ZHANG P, QI J, et al. HAT: hierarchical aggregation transformers for person re-identification[C]//Proceedings of the 29th ACM International Conference on Multimedia. New York: ACM, 2021: 516-525. [21] 刘洋, 闫冬梅, 孟范伟. 基于Transformer改进的两分支行人重识别算法[J]. 东北大学学报 (自然科学版), 2023, 44(1): 26-32. LIU Y, YAN D M, MENG F W. Improved two-branch person re-identification algorithm based on transformer[J]. Journal of Northeastern University (Natural Science), 2023, 44(1): 26-32. [22] ZHANG R. Making convolutional networks shift-invariant again[C]//Proceedings of the 2019 International Conference on Machine Learning, Los Angeles, 2019: 7324-7334. [23] ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification: a benchmark[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, 2015: 1116-1124. [24] ZHENG Z, ZHENG L, YANG Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, 2017: 3754-3762. [25] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2921-2929. [26] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 3-19. [27] FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008: 1-8. [28] ZHONG Z, ZHENG L, KANG G, et al. Random erasing data augmentation[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 13001-13008. [29] KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 1725-1732. [30] HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 770-778. [31] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, 2017: 618-626. [32] WU J, YANG Y, LEI Z, et al. Camera-aware representation learning for person re-identification[J]. Neurocomputing, 2023, 518: 155-164. [33] CHEN T L, DING S J, XIE J Y, et al. ABD-net: attentive but diverse person re-identification[C]//Proceedings of the 2019 IEEE International Conference on Computer Vision, Seoul, 2019: 8351-8361. [34] ZHOU K Y, YANG Y X, CAVALLARO A, et al. Omni-scale feature learning for person re-identification[C]//Proceedings of the 2019 IEEE International Conference on Computer Vision, Seoul, 2019: 3702-3712. [35] ZHU K, GUO H, LIU Z, et al. Identity-guided human semantic parsing for person re-identification[C]//Proceedings of the 16th European Conference on Computer Vision, Glasgow, 2020: 346-363. [36] 王鹏, 宋晓宁, 吴小俊, 等. 用于行人重识别的多类型特征网络[J]. 模式识别与人工智能, 2020, 33(10): 879-888. WANG P, SONG X N, WU X J, et al. Multi-type features network for person re-identification[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(10): 879-888. [37] YE M, SHEN J, LIN G, et al. Deep learning for person re-identification: a survey and outlook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(6): 2872-2893. [38] ZHONG Z, ZHENG L, CAO D, et al. Re-ranking person re-identification with k?reciprocal encoding[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pttern Rcognition, Honolulu, 2017: 1318-1327. [39] LI Y, HE J, ZHANG T, et al. Diverse part discovery: occluded person re-identification with part-aware transformer[C]//Proceedings of the 2021 IEEE Conference on Computer Vision and Pattern Recognition, 2021: 2898-2907. [40] CHEN Y, XIA S, ZHAO J, et al. ResT-reID: transformer block-based residual learning for person re-identification[J]. Pattern Recognition Letters, 2022, 157: 90-96. |
[1] | TAO Linjuan, HUA Gengxing, LI Bo. Aspect-Level Sentiment Analysis Based on Location-Enhanced Word Embeddings and GRU-CNN Model [J]. Computer Engineering and Applications, 2024, 60(9): 212-218. |
[2] | LIU Shipeng, NING Dejun, MA Jue. LSTformer Model for Photovoltaic Power Prediction [J]. Computer Engineering and Applications, 2024, 60(9): 317-325. |
[3] | WANG Ru, LIU Daming, ZHANG Jian. Wear-YOLO:Research on Detection Methods of Safety Equipment for Power Personnel in Substations [J]. Computer Engineering and Applications, 2024, 60(9): 111-121. |
[4] | CAI Teng, CHEN Cifa, DONG Fangmin. Low-Light Object Detection Combining Transformer and Dynamic Feature Fusion [J]. Computer Engineering and Applications, 2024, 60(9): 135-141. |
[5] | YANG Wentao, LEI Yuqi, LI Xingyue, ZHENG Tiancheng. Chinese Long Text Classification Model Based on BERT Fused Chinese Input Methods and BLCG [J]. Computer Engineering and Applications, 2024, 60(9): 196-202. |
[6] | GUO Jin, SONG Tingqiang, SUN Yuanyuan, GONG Chuanjiang, LIU Yalin, MA Xinglu, FAN Haisheng. Improved Deeplabv3+ Crop Classification Method Based on Double Attention Fusion [J]. Computer Engineering and Applications, 2024, 60(8): 110-120. |
[7] | ZOU Zhentao, LI Zeping. Improved YOLOv7 for UAV Image Object Detection [J]. Computer Engineering and Applications, 2024, 60(8): 173-181. |
[8] | CHANG Xilong, LIANG Kun, LI Wentao. Review of Development of Deep Learning Optimizer [J]. Computer Engineering and Applications, 2024, 60(7): 1-12. |
[9] | PIAN Xinyang, WANG Yu, ZHANG Jie. Applying Attention Transformer Module to 3D Lip Sequence Identification [J]. Computer Engineering and Applications, 2024, 60(7): 141-146. |
[10] | WANG Lei, YANG Jun, ZHANG Chiyu, DAI Zaiyan. Generative Adversarial Network with Dual Discriminator and Mixed Attention [J]. Computer Engineering and Applications, 2024, 60(7): 212-221. |
[11] | LIU Xinning. Medical Named Entity Recognition Based on Multi-Feature and Co-Attention [J]. Computer Engineering and Applications, 2024, 60(6): 188-198. |
[12] | FANG Siyan, LIU Bin. Wavelet Frequency Division Self-Attention Transformer Image Deraining Network [J]. Computer Engineering and Applications, 2024, 60(6): 259-273. |
[13] | CAI Guoyong, LI Anqing. Prompt-Learning Inspired Approach to Unsupervised Sentiment Style Transfer [J]. Computer Engineering and Applications, 2024, 60(5): 146-155. |
[14] | FANG Hong, LI Desheng, JIANG Guangjie. Efficient Cross-Domain Transformer Few-Shot Semantic Segmentation Network [J]. Computer Engineering and Applications, 2024, 60(4): 142-152. |
[15] | GUAN Wenqing, ZHOU Shibin, ZHANG Guopeng. Aerial Image Object Detection with Feature Enhancement Using Hybrid Attention [J]. Computer Engineering and Applications, 2024, 60(4): 249-257. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||