Cited by 1,402Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

The DOI matches its Crossref record; 41 of the 45 references it cites were found in a registry or an index.

Stacked Cross Attention for Image-Text Matching

Open access

Lee, Kuang-Huei, Chen, Xi, Hua, Gang, Hu, Houdong, He, Xiaodong·2018·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-01225-0_13

Open this paper in CoChat — free account Open PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/978-3-030-01225-0_13

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248

Show all 45 references

Research works printed but not linked: 6

Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) · arXiv
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: NIPS (2015)
Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: improved visual-semantic embeddings. arXiv preprint arXiv:1707.05612 (2017) · arXiv
Peng, Y., Qi, J., Yuan, Y.: CM-GANs: cross-modal generative adversarial networks for common representation learning. arXiv preprint arXiv:1710.05106 (2017) · arXiv
Zheng, Z., Zheng, L., Garrett, M., Yang, Y., Shen, Y.D.: Dual-path convolutional image-text embedding. arXiv preprint arXiv:1711.05535 (2017) · arXiv

Sorted by citations

Items 901–1,000 of 1,001

CroMIC-QA: The Cross-Modal Information Complementation Based Question Answering
湯浅, 順., Liu, B., Sun, C., Xu, Z. X., Ma, L., & Wang, B. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3326616 · Cited by 3
Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms
Li, Y., Wang, Q., & Jia, K. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.047822 · Cited by 3
Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder
Zhou, K., Xing, Y., Geng, Y., & Zhao, J. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651513 · Cited by 3
Cross-modal retrieval of chest X-ray images and diagnostic reports based on report entity graph and dual attention
Ou, W., Chen, Y., Liang, L., Gou, J., Xiong, J., Zhang, J., Lai, L., & Zhang, L. (2025) · Multimedia Systems · doi:10.1007/s00530-024-01649-6 · Cited by 4
Fine-Grained Local and Global Semantic Fusion for Multimodal Image–Text Retrieval
Peng, S., Wang, Z., Liu, J., Zhang, C., & Jia, L. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030053 · Cited by 3
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding
Xu, W., Weng, W., Zhang, Y., & Xiong, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92460-6_1 · Cited by 4
PKE-UIC: A Prior Knowledge-Enhanced Underwater Image Captioning Model
Li, H., Zhang, W., Li, Z., & Ren, P. (2024) · International Conference on Sensors, Signal and Image Processing (SSIP) · doi:10.1145/3725949.3725952 · Cited by 3
Hashing for Image Geolocalization of Street Views
Liu, J., He, K., & Ren, P. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3606968 · Cited by 3
Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Retrieval
Liang, Z., Liang, M., Huang, W. P., Li, Y., Liu, W., Shao, Y., & Lu, K. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754844 · Cited by 4
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation for Few-Shot Cross-Modal Retrieval
Sun, C., Li, W., Yuan, X., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5567-3_14 · Cited by 3
Multimodal Neural Machine Translation of Fashion E-Commerce Descriptions
Laenen, K., & Moens, M. (2019) · Springer eBooks · doi:10.1007/978-3-030-15436-3_4 · Cited by 3
Fine-grained Cross-media Representation Learning with Deep Quantization Attention Network
Liang, M., Du, J., Liu, W., Xue, Z., Geng, Y., & Yang, C. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350892 · Cited by 3
Cross-Media Image-Text Retrieval Combined with Global Similarity and Local Similarity
Li, Z., Ling, F., & Zhang, C. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00029 · Cited by 3
Component Analysis for Visual Question Answering Architectures
dos Reis, C. K., Wehrmann, J., & Barros, R. C. (2020) · PUCRS Repository (Pontifical Catholic University of Rio Grande do Sul) · Cited by 3
Fine-Grained Image-Text Retrieval via Complementary Feature Learning
Zheng, M., Jia, Y., & Jiang, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_48 · Cited by 3
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
Li, R., Li, K., Wang, L., & Hua, K. A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412297 · Cited by 3
Grounded Vocabulary for Image Retrieval Using a Modified Multi-Generator Generative Adversarial Network
Kim, K., Park, C., Seo, J., & Lim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3122547 · Cited by 3
Multiple Context Learning Networks for Visual Question Answering
Zhang, P., Lan, H., & Khan, M. A. (2022) · Scientific Programming · doi:10.1155/2022/4378553 · Cited by 3
Multimodal Retrieval in E-Commerce
Hendriksen, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99739-7_62 · Cited by 3
Object Prior Embedded Network for Query-Agnostic Image Retrieval
Li, Y., Hsiao, J.-H., & Ho, C. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00544 · Cited by 3
Multi-Attention Cascade Model Based on Multi-Head Structure for Image-Text Retrieval
Zhang, H., Wu, W. B., & Zhang, M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891983 · Cited by 3
AnANet: Association and Alignment Network for Modeling Implicit Relevance in Cross-Modal Correlation Classification
Xu, N., Wang, J., Tian, Y., Zhang, R., & Mao, W. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3229960 · Cited by 3
Semantic-embedding Guided Graph Network for cross-modal retrieval
Yuan, M., Zhang, H., Liu, D., Wang, L., & Liu, L. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103807 · Cited by 3
Dual-grained Text-Image Olfactory Matching Model with Mutual Promotion Stages
Shao, Y., Sun, J., Jiang, Y., & Li, J. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587649 · Cited by 3
FGCVQA: Fine-Grained Cross-Attention for Medical VQA
Wu, Z., Shu, X., Yan, S., & Lu, Z. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222540 · Cited by 3
Domain-Adaptive Vision Transformers for Generalizing Across Visual Domains
Cho, Y., Yun, J., Kwon, J., & Kim, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3324545 · Cited by 3
External Knowledge Dynamic Modeling for Image-text Retrieval
Yang, S., Li, Q., Li, W., Liu, M., Li, X., & Liu, A.-A. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613786 · Cited by 3
Thangka Image—Text Matching Based on Adaptive Pooling Layer and Improved Transformer
Wang, K., Wang, T., Guo, X., Kui, X., & Wu, J. (2024) · Applied Sciences · doi:10.3390/app14020807 · Cited by 3
An empirical study of excitation and aggregation design adaptions in CLIP4Clip for video–text retrieval
Jing, X., Yang, G., & Chu, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127905 · Cited by 3
Attentive Fusion for Efficient Wrist-Worn Gesture Recognition Based on Dual-View Cameras
Quan, P., Mao, Z., Chen, N., Zhang, Y., Zhang, K., & Pan, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3500576 · Cited by 3
DCL-net: Dual-level correlation learning network for image–text retrieval
Liu, Z., Li, A., Xu, J., & Shi, D. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.110000 · Cited by 3
LFSRM: Few-Shot Diagram-Sentence Matching via Local-Feedback Self-Regulating Memory
Zhang, L., Wu, W., Liu, J., Chang, X., Hu, X., Luo, X., Wu, Y., & Zheng, Q. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3528723 · Cited by 3
CausMatch: Causal Matching Learning With Counterfactual Preference Framework for Cross-Modal Retrieval
Chen, C., & Wang, D. (2025) · IEEE Access · doi:10.1109/access.2025.3529942 · Cited by 3
Script-to-Storyboard: A New Contextual Retrieval Dataset and Benchmark
Tian, X., Yang, Y.-L., & Wu, Q. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450322 · Cited by 3
Solar Multimodal Transformer: Intraday Solar Irradiance Predictor Using Public Cameras and Time Series
Niu, Y., Sarkis, R., Psaltis, D., Paolone, M., Moser, C., & Lambertini, L. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00494 · Cited by 4
Adapted Graph Reasoning and Filtration for Description-Image Retrieval
Chen, S., Luo, Z., Gao, Y., Zhou, W., Li, C., & Chen, H. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463047 · Cited by 3
Image-Text Matching: Methods and Challenges
Abdullah, T., & Rangarajan, L. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-1395-1_17 · Cited by 3
Adaptive Ladder Loss for Learning Coherent Visual-Semantic Embedding
Wang, L., Zhou, M., Niu, Z., Zhang, Q., & Zheng, N. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3139210 · Cited by 3
Enhanced Attribute Alignment Based on Semantic Co-Attention for Text-Based Person Search
Wang, H., & Hu, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93046-2_53 · Cited by 3
Disentangled OCR: A More Granular Information for “Text”-to-Image Retrieval
Zhou, X., Li, S., Chen, H., & Zhu, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18907-4_40 · Cited by 3
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Wang, T.-J. J., Laaksonen, J. T., Langer, T., Arponen, H., & Bishop, T. E. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00113 · Cited by 3
FB-Net: Dual-Branch Foreground-Background Fusion Network With Multi-Scale Semantic Scanning for Image-Text Retrieval
Xu, J., Liu, Z., Pei, X., Wang, S., & Gao, S. (2023) · IEEE Access · doi:10.1109/access.2023.3263512 · Cited by 3
TECMH: Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval
Li, Q., Ma, L., Jiang, Z., Li, M., & Jin, B. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037463 · Cited by 4
Visual Semantic Parsing: From Images to Abstract Meaning Representation
Abdelsalam, M. A., Shi, Z., Fancellu, F., Basioti, K., Bhatt, D., Pavlović, V., & Fazly, A. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.19 · Cited by 3
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification
Xu, C., & Li, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.381 · Cited by 3
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval
Ueki, K. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00105 · Cited by 4
SPSD: Similarity-preserving self-distillation for video–text retrieval
Wang, J., Yan, H., Yang, Y., & Kou, H. (2023) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-023-00298-1 · Cited by 3
Cross-Shape Graph Convolutional NetworksPreprint
Petrov, D., & Kalogerakis, E. (2020) · arXiv (Cornell University) · Cited by 3
Personalized Multi-modal Video Retrieval on Mobile Devices
Zhang, H., Jepson, A. D., Mohomed, I., Derpanis, K. G., Zhang, R., & Fazly, A. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3481545 · Cited by 2
Locally controllable network based on visual–linguistic relation alignment for text-to-image generation
Li, Z., Liu, L., Zhang, H., Liu, D., Song, Y., & Li, B. (2024) · Multimedia Systems · doi:10.1007/s00530-023-01222-7 · Cited by 2
Research on type-aware fashion compatibility prediction based on a hybrid attention mechanism
Li, Y., Li, G., Zhang, J., Jing, P., & Lu, X. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18101-y · Cited by 2
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysis
Williams-Lekuona, M., & Cosma, G. (2025) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-025-00368-6 · Cited by 2
Learning Multi-Scale Knowledge-Guided Features for Text-Guided Face Recognition
Hasan, M. M., Sami, S. M., Nasrabadi, N. M., & Dawson, J. M. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3466216 · Cited by 2
AsCL: An Asymmetry-sensitive Contrastive Learning Method for Image-Text Retrieval with Cross-Modal Fusion
Gong, Z., Mai, C., & Huang, Y. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687993 · Cited by 2
Towards Maximizing Semantic Coverage for Image-Text Retrieval
Xu, J., Liu, Z., Chen, Z., & Shen, F. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888503 · Cited by 3
CSA: Cross-scale alignment with adaptive semantic aggregation and filter for image–text retrieval
Liu, Z., Xu, J., Gao, S., & Chen, Z. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111647 · Cited by 2
Disentangled Noisy Correspondence Learning
Dang, Z., Luo, M., Wang, J., Jia, C., Han, H., Wan, H., Dai, G., Chang, X., & Wang, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3559457 · Cited by 2
Multi-Scale Feature Fusion Based on Piecewise Polynomial Activation Function for Image-Text Matching
Ji, L., & Liu, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3571731 · Cited by 2
Progressive multi-subspace fusion for text-image matching
Wang, H., Zhu, L., Ma, W., & Guo, Q. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01946-1 · Cited by 2
VLMs bridging-enhanced Scene Semantic Reasoning Framework for Image-Text Matching
Gao, Y., Chen, J., & Li, M. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733465 · Cited by 2
Spatially-Aware Entity Relation Exploration for Remote Sensing Image-Text Retrieval
Shui, J., Ding, S., Ge, M., & Li, M. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733434 · Cited by 3
FAME: Fusion of Alignment and Multiview Enhancement for Remote Sensing Image–Text Retrieval
Su, Y., Zhu, D., Dong, Z., Lin, Q., Liu, L., & Bao, S. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3610428 · Cited by 2
OpenEvents V1: Large-Scale Benchmark Dataset for Multimodal Event Grounding
Nguyen, H. X., Nguyen, P.-T., Tran, T.-P., Nguyen, M.-Q., Nguyen, T., Tran, M., & Le, T.-N. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758264 · Cited by 7
Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models
Zhang, W., & Tao, J. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3634650 · Cited by 3
ColorSketchNet: Unifying color, sketch and texture for modality-agnostic multi-modal person re-identification
Liu, M., Cheng, X., Jiang, Y. L., & Wang, B. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108374 · Cited by 2
Redundancy Mitigation: Toward Accurate and Efficient Image–Text Retrieval
Wang, K., Hu, Y., Liu, H., Jie, L., & Nie, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3643601 · Cited by 3
A review for image–text matching from deep learning perspective
Dong, C., & Wei, C. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103453 · Cited by 2
Cross-Modal Alignment Enhancement Network for Text-to-Image Person Re-Identification
He, D., Zhu, X., Li, B., Yue, S., & Zhang, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3621864 · Cited by 2
ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval
Zhan, G., Liu, Y., Han, K., Xie, W., & Zisserman, A. (2025) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi66578.2025.11339290 · Cited by 5
Fine-Grained Multimodal Alignment for Image-Text Retrieval via Graph Learning
Chen, M., Zhang, X., Qi, L., Li, X., Yang, X., Hoi, S. C. H., Liu, Z., & Yang, M. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02686-y · Cited by 2
Aleatoric-Epistemic Joint Uncertainty Modeling for Cross-Modal Retrieval
Chang, T., Song, P., Yang, X., Guo, D., & Chang, X. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3664380 · Cited by 2
Multi-Granularity Modal Interaction and Fusion framework for vision-language tasks
Xu, Y., Liu, G., Shen, X., Wang, X., & Zhou, H. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114849 · Cited by 2
Resolving multi-level cross-modal semantic ambiguity for remote sensing image–text retrieval
Chen, S., Wu, C., Wang, J., Ma, W., He, J., Shi, G., & Gu, L. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116827 · Cited by 2
Dual Semantic Relationship Attention Network for Image-Text Matching
Wen, K., & Gu, X. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206782 · Cited by 2
DRSGN: Dual Revised Semantic Graph Structured Network for Image-Text Matching
Yang, X., Wu, X., & Xu, T. (2021) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis53392.2021.9754625 · Cited by 2
Modaldrop: Modality-Aware Regularization for Temporal-Spectral Fusion in Human Activity Recognition
Zeng, X., Chen, Y., Xu, B., & Zhang, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095880 · Cited by 2
LET: Leveraging Error Type Information for Grammatical Error Correction
Yang, L., Li, H., Li, L., Xu, C., Xia, S., & Yuan, C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.371 · Cited by 2
Asymmetric bi-encoder for image–text retrieval
Xiong, W., Liu, H., Mi, S., & Zhang, Y. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01162-2 · Cited by 2
Global-to-Contextual Shared Semantic Learning for Fine-Grained Vision-Language Alignment
Zheng, M., Wu, C., Qin, J., Liu, W., Chen, M., Lin, L., & Zhou, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44198-1_24 · Cited by 2
Neural-Based Cross-Modal Search and Retrieval of Artwork
Gong, Y., Cosma, G., & Finke, A. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371948 · Cited by 2
A Combined Alignment Model for Code Search
Hong, J., Choi, E., & Mizuno, O. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023mpp0002 · Cited by 2
Does the Performance of Text-to-Image Retrieval Models Generalize Beyond Captions-as-a-Query?
Rodríguez, J. M., Tavassoli, N., Levy, E., Lederman, G., Sivov, D., Lissandrini, M., & Mottin, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_15 · Cited by 2
FELGA: Unsupervised Fragment Embedding for Fine-Grained Cross-Modal Association
Zhuo, Y., & Li, B. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00554 · Cited by 2
Adaptive Visual Semantic Embedding with Adaptive Pooling and Instance-Level Interaction
Chen, J., Zhu, Y., Yao, H., & Zhang, W. (2023) · International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE) · doi:10.1109/icemce60359.2023.10490683 · Cited by 2
MMFG: Multimodal-based Mutual Feature Gating 3D Object Detection
Xu, W., & Fu, Z. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02119-x · Cited by 2
Multi-view and region reasoning semantic enhancement for image-text retrieval
Cheng, W., Han, Z., He, D., & Wu, L. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01383-z · Cited by 2
Parallel weight control based on policy gradient of relation refinement for cross-modal retrieval
Zhang, L., Yang, Y., Ge, S., Sun, G., & Wu, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108949 · Cited by 2
Graph-based Consistent Reconstruction and Alignment for imbalanced text–image person re-identification
Du, G., Gong, T., & Zhang, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125429 · Cited by 2
Selection and Reconstruction of Key Locals: A Novel Specific Domain Image-Text Retrieval Method
Liao, Y., ZHANG, X., Yang, R., Tao, J., Liu, B., Hu, Z., Wang, S., & Zhao, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681421 · Cited by 3
GNEM: Comprehensive Similarity Learning With Ensemble Model for Code Search
Hong, J., Choi, E., & Mizuno, O. (2024) · IEEE Access · doi:10.1109/access.2024.3493193 · Cited by 2
Text-to-image Retrieval Based on Zero-shot Transfer Learning with CLIP Model and Vector Database
Xie, J., & Chen, J. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780701 · Cited by 2
Syntactic-guided optimization of image–text matching for intra-modal modeling
Wu, D., Zhang, L., & Chen, Y. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-024-06840-0 · Cited by 2
ReAL : Improving Image-Text Retrieval with Authentic Negative Repository Learning
Pan, R., Yang, H., & Zhao, X. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3729172 · Cited by 3
Enhancing and Accelerating Image-Text Retrieval with Knowledge Graphs and FAISS
Wang, J., Zeng, J., & Sheng, J. (2024) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat62293.2024.00102 · Cited by 2
Text semantic-guided adaptive feature aggregation for image-text retrieval
Gu, Y., Wang, M., Gan, J., Zhao, Y., Mei, J., & Zhang, C. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01881-8 · Cited by 2
Tokenfocus-VQA: Enhancing Text-to-Image Alignment with Position-Aware Focus and Multi-Perspective Aggregations on LVLMs
Zhang, Z., Zheng, X., Wu, X., Peng, C., & Cao, X. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00120 · Cited by 2
MSSA: A Multi-Scale Semantic-Aware Method for Remote Sensing Image–Text Retrieval
Liao, Y., Hu, Z., Jin, F., Liu, J., Chen, N., Lv, J., & Duan, Q. (2025) · Remote Sensing · doi:10.3390/rs17193341 · Cited by 2
Cross-media Image-Text Retrieval Based on Two-Level Network
Li, Z., Ling, F., Zhang, F., & Zhang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_18 · Cited by 2
Kindai University and Kobe University at TRECVID 2019 AVS Task.
Shirahama, K., Sakurai, D., Matsubara, T., & Uehara, K. (2019) · TRECVID · Cited by 2
What Did This Castle Look like before? Exploring Referential Relations in Naturally Occurring Multimodal Texts
Utescher, R., & Zarrieß, S. (2021) · PUB – Publications at Bielefeld University (Bielefeld University) · Cited by 2

Authors: Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X.
Affiliations: Microsoft (United States) (US)
Jingdong (China) (CN)
Corresponding author: Lee, Kuang-Huei
ORCID: Chen, Xi
Hua, Gang
He, Xiaodong
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2018
Pages: 212-228
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-01225-0_13
OpenAlex: W2962964995
Cited by: 1,402
References: 45
Citations by year: 2026: 104 · 2025: 202 · 2024: 244 · 2023: 241 · 2022: 201 · 2021: 211 · 2020: 131 · 2019: 63 · 2018: 5
FWCI: 30.89
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords: image-text matching, cross-attention, vision-language alignment, image retrieval, text retrieval, interpretability, MS COCO, Flickr30k

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Stacked Cross Attention for Image-Text Matching

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks