Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (5): 145-157.doi: 10.16088/j.issn.1001-6600.2024112901
• Intelligence Information Processing • Previous Articles Next Articles
LUO Zengli1, ZHANG Canlong1,2*, LI Zhixin1,2, WANG Zhiwen3, WEI Chunrong4
| [1] JING Y, WANG W, WANG L, et al. Cross-modal cross-domain moment alignment network for person search[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 10675-10683. DOI: 10.1109/CVPR42600.2020.01069. [2] WANG Z, FANG Z Y, WANG J, et al.ViTAA: visual-textual attributes alignment in person search by natural language[C]// Computer Vision-ECCV 2020. Cham: Springer International Publishing, 2020: 402-420. DOI: 10.1007/978-3-030-58610-2_24. [3] 肖逸群, 宋树祥, 夏海英. 基于多特征的快速行人检测方法及实现[J]. 广西师范大学学报(自然科学版), 2019, 37(4): 61-67. DOI: 10.16088/j.issn.1001-6600.2019.04.007. [4] 周东明, 张灿龙, 唐艳平, 等. 联合语义分割与注意力机制的行人再识别模型[J]. 计算机工程, 2022, 48(2): 201-206. DOI: 10.19678/j.issn.1000-3428.0060416. [5] WU Y S, YAN ZZ, HAN X G, et al. LapsCore: language-guided person search via color reasoning[C]// 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2021: 1604-1613. DOI: 10.1109/ICCV48922.2021.00165. [6] GAO C Y, CAI G Y, JIANG X Y, et al. Contextual non-local alignment over full-scale representation for text-based person search[EB/OL].(2021-01-08)[2024-11-29]. https://arxiv.org/abs/2101.03036. DOI: 10.48550/arXiv.2101.03036. [7] 李大伟, 曾智勇. 基于动态双注意力机制的跨模态行人重识别模型[J]. 计算机应用, 2022, 42(10): 3200-3208. DOI: 10.11772/j.issn.1001-9081.2021081510. [8] 邓淑雅, 李浩源. 基于注意力特征融合的跨模态行人重识别[J]. 计算机系统应用, 2024, 33(9): 269-275. DOI: 10.15888/j.cnki.csa.009604. [9] YANG X, WANG X Q, WANG N N, et al. Address the unseen relationships: attribute correlations in text attribute person search[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(11): 16916-16926. DOI: 10.1109/TNNLS.2023.3300582. [10] 张雯欣, 刘玉杰, 王兆勇, 等. 基于原型分散网络的端到端行人搜索方法[J]. 计算机工程, 2025, 51(1): 269-276. DOI: 10.19678/j.issn.1000-3428.0068462. [11] ZHENG Y W, ZHAO X P, LAN C L, et al. CPCL: cross-modal prototypical contrastive learning for weakly supervised text-based person re-identification[EB/OL].(2024-01-18)[2024-11-29]. https://arxiv.org/abs/2401.10011. DOI: 10.48550/arXiv.2401.10011. [12] SHAO Z Y, ZHANG X Y, DING C X, et al. Unified pre-training with pseudo texts for text-to-image person re-identification[C]// 2023 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2023: 11140-11150. DOI: 10.1109/ICCV51070.2023.01026. [13] YANG S Y, ZHOU Y N, ZHENG Z D, et al. Towards unified text-based person retrieval: a large-scale multi-attribute and language search benchmark[C]// MM'23: Proceedings of the 31st ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery, 2023: 4492-4501. DOI: 10.1145/3581783.3611709. [14] 郭玉彬, 文向, 刘攀, 等. 基于双流结构的跨模态行人重识别关系网络[J]. 计算机应用, 2023, 43(6): 1803-1810. DOI: 10.11772/j.issn.1001-9081.2022050665. [15] 贾军营, 杨芯茹, 杨海波, 等. 改进CLIP-ReID的跨模态行人重识别[J]. 计算机系统应用, 2025, 34(1): 153-160. DOI: 10.15888/j.cnki.csa.009741. [16] 何嘉明, 杨巨成, 吴超, 等. 基于多模态图卷积神经网络的行人重识别方法[J]. 计算机应用, 2023, 43(7): 2182-2189. DOI: 10.11772/j.issn.1001-9081.2022060827. [17] 李灏, 唐敏, 林建武, 等. 基于改进困难三元组损失的跨模态行人重识别框架[J]. 计算机科学, 2020, 47(10): 180-186. DOI:10.11896/jsjkx.191100061. [18] 姜定, 叶茫. 面向跨模态文本到图像行人重识别的Transformer网络[J]. 中国图象图形学报, 2023, 28(5): 1384-1395. [19] LI S, XIAO T, LI H S, et al. Person search with natural language description[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 5187-5196. DOI: 10.1109/CVPR.2017.551. [20] ZHU A C, WANG Z J, LI Y F, et al. DSSL: deep surroundings-person separation learning for text-based person retrieval[C]// MM'21: Proceedings of the 29th ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery, 2021: 209-217. DOI: 10.1145/3474085.3475369. [21] DING Z F, DING C X, SHAO Z Y, et al. Semantically self-aligned network for text-to-image part-aware person re-identification[EB/OL].(2021-08-09)[2024-11-29]. https://arxiv.org/abs/2107.12666. DOI: 10.48550/arXiv.2107.12666. [22] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2019: 4171-4186. DOI: 10.18653/v1/N19-1423. [23] RADFORD A, KIM J W, HALLACY C, et al. Learning transferable visual models from natural language supervision[C]// Proceedings of the 38th International Conference on Machine Learning: PMLR 139. Cambridge MA: JMLR, 2021: 8748-8763. [24] YAN S L, DONG N, ZHANG L Y, et al. CLIP-driven fine-grained text-image person re-identification[J]. IEEE Transactions on Image Processing, 2023, 32: 6032-6046. DOI: 10.1109/TIP.2023.3327924. [25] JIANG D, YE M. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2023: 2787-2797. DOI: 10.1109/CVPR52729.2023.00273. [26] ZHOU J F, HUANG B G, FAN W J, et al. Text-based personsearch via local-relational-global fine grained alignment[J]. Knowledge-Based Systems, 2023, 262: 110253. DOI: 10.1016/j.knosys.2023.110253. [27] CAO M, BAI Y, ZENG Z Y, et al. An empirical study of CLIP for text-based person search[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(1): 465-473. DOI: 10.1609/aaai.v38i1.27801. [28] LI Z, SI L J, GUO C L, et al. Data augmentation for text-based person retrieval using large language models[EB/OL].(2024-05-20)[2024-11-29]. https://arxiv.org/abs/2405.11971. DOI: 10.48550/arXiv.2405.11971. [29] BAO L P, WEI L H, ZHOU W G, et al.Multi-granularity matching transformer for text-based person search[J]. IEEE Transactions on Multimedia, 2023, 26: 4281-4293. DOI: 10.1109/TMM.2023.3321504. [30] LI S S, XU X, SHEN F M, et al. Multi-granularity separation network for text-based person retrieval with bidirectional refinement regularization[C]// ICMR'23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval. New York, NY: Association for Computing Machinery, 2023: 307-315. DOI: 10.1145/3591106.3592253. [31] GAO L Y, NIU K, JIAO B L, et al. Addressing information inequality for text-based person search via pedestrian-centric visual denoising and bias-aware alignments[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(12): 7884-7899. DOI: 10.1109/TCSVT.2023.3273719. [32] MA Y W, SUN X S, JI J Y, et al. Beat: bi-directional one-to-many embedding alignment for text-based person retrieval[C]// MM'23: Proceedings of the 31st ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery, 2023: 4157-4168. DOI: 10.1145/3581783.3611768. [33] CHEN Y C, LI L J, YU L C, et al. UNITER: universal image-text representation learning[C]// Computer Vision-ECCV 2020. Cham: Springer International Publishing, 2020: 104-120. DOI: 10.1007/978-3-030-58577-8_7. [34] LI J N, SELVARAJU R, GOTMARE A, et al. Align before fuse: vision and language representation learning with momentum distillation[C]// Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Red Hook, NY: Curran Associates Inc., 2021: 9694-9705. [35] ZANG X H, GAO W, LI G, et al. A baseline investigation: transformer-based cross-view baseline for text-based person search[C]// MM'23: Proceedings of the 31st ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery, 2023: 7737-7746. DOI: 10.1145/3581783.3611916. [36] YAN S L, TANG H, ZHANG L Y, et al. Image-specific information suppression and implicit local alignment for text-based person search[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(12): 17973-17986. DOI: 10.1109/TNNLS.2023.3310118. [37] CHEN W H, XU X Z, JIA J, et al. Beyond appearance: a semantic controllable self-supervised learning framework for human-centric visual tasks[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2023: 15050-15061. DOI: 10.1109/CVPR52729.2023.01445. [38] LIU H T, LI C Y, WU Q Y, et al. Visual instruction tuning[C]// Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Red Hook, NY: Curran Associates Inc., 2023: 1516. [39] HU E J, SHEN Y L, WALLIS P, et al. LoRA: low-rank adaptation of large language models[EB/OL]. (2021-10-16)[2024-11-29]. https://arxiv.org/abs/2106.09685. DOI: 10.48550/arXiv.2106.09685. [40] DEB K, GUPTA H. Searching for robust Pareto-optimal solutions in multi-objective optimization[C]// Evolutionary Multi-Criterion Optimization. Berlin: Springer, 2005: 150-164. DOI: 10.1007/978-3-540-31880-4_11. [41] CARON M, TOUVRON H, MISRA I, et al. Emerging properties in self-supervised vision transformers[C]// 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2021: 9630-9640. DOI: 10.1109/ICCV48922.2021.00951. [42] LI J N, LI D X, XIONG C M, et al. BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation[C]// Proceedings of the 39th International Conference on Machine Learning: PMLR 162. Cambridge MA: JMLR, 2022: 12888-12900. [43] HUANG L, YU W J, MA W T, et al. A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions[J]. ACM Transactions on Information Systems, 2025, 43(2):42. DOI: 10.1145/3703155. |
| [1] | DU Jinfeng, WANG Hairong, LIANG Huan, WANG Dong. Progress of Cross-modal Retrieval Methods Based on Representation Learning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 1-12. |
|