The DOI matches its Crossref record; 79 of the 85 references it cites were found in a registry or an index.
Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers
Open accessAbstract
Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformer-based models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and reranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset.
Overview
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
Cites
Statistics
Verification
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?
In the citing paper
“This has been studied for more challenging and fine-grained tasks (e.g., object detection [269], visual question answering [103], [106], [112], [263], and instance retrieval [222], [263]) by imposing region (semantic parts such as objects) level alignment.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [269]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
(2011) · Cited by 14,317
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841 · cited 2×
“We choose a lightweight architecture for this upsampling process inspired by recent advances in efficient object detection [69].”
MethodVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Locality-sensitive hashing scheme based on p-stable distributions
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2004) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/997817.997857 · Cited by 2,971
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956
Object retrieval with large vocabularies and fast spatial matching
Philbin, J. F., Chum, O., Isard, M., Šivic, J., & Zisserman, A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383172 · Cited by 2,931
Self-citationFAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
(2009) · Proceedings of the Fourth International Conference on Computer Vision Theory and Applications · doi:10.5220/0001787803310340 · Cited by 2,640
Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319
TSM: Temporal Shift Module for Efficient Video Understanding
Lin, J., Gan, C., & Han, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00718 · Cited by 2,209
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109
Buciluǎ, C., Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150464 · Cited by 2,068
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Hudson, D. A., & Manning, C. D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00686 · Cited by 1,797
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X. P., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Li, D., Wei, F., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,791
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. U., & Hyvärinen, A. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 1,588
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444
Exploring the Limits of Language ModelingPreprint
Józefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., & Wu, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.02410 · Cited by 1,238
VisualBERT: A Simple and Performant Baseline for Vision and Language
Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019) · arXiv · doi:10.48550/arxiv.1908.03557 · Cited by 1,198
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00272 · Cited by 986
Self-citationVL-BERT: Pre-training of Generic Visual-Linguistic Representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv · doi:10.48550/arxiv.1908.08530 · Cited by 950
Sampling Matters in Deep Embedding Learning
Wu, C.-Y., Manmatha, R., Smola, A. J., & Krähenbühl, P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.309 · Cited by 919
Unified Vision-Language Pre-Training for Image Captioning and VQA
Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7005 · Cited by 864
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Chum, O., Philbin, J. F., Šivic, J., Isard, M., & Zisserman, A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408891 · Cited by 861
Self-citationLearning Deep Structure-Preserving Image-Text Embeddings
Wang, L., Li, Y., & Lazebnik, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.541 · Cited by 795
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training
Li, G., Duan, N., Fang, Y., Gong, M., & Jiang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6795 · Cited by 795
Structured Knowledge Distillation for Semantic Segmentation
Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., & Wang, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00271 · Cited by 732
End-to-End Learning of Visual Representations From Uncurated Instructional Videos
Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Šivic, J., & Zisserman, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00990 · Cited by 646
Self-citationJointly Modeling Embedding and Translation to Bridge Video and Language
Pan, Y., Mei, T., Yao, T., Li, H., & Rui, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.497 · Cited by 572
Learning from Noisy Labels with Distillation
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., & Li, L.-J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.211 · Cited by 557
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics
Gong, Y., Ke, Q., Isard, M., & Lazebnik, S. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0658-4 · Cited by 549
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Wang, L., Li, Y., Huang, J. L., & Lazebnik, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2797921 · Cited by 548
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
Wang, X., Wu, J., Chen, J., Li, L., Wang, Y., & Wang, W. Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00468 · Cited by 536
Cross Modal Distillation for Supervision Transfer
Gupta, S., Hoffman, J., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.309 · Cited by 530 · cited 1×
“ing [41, 61], depth estimation [22], action recognition [66], semantic segmentation [44], self-supervised learning [58] or self-training [76].”
BackgroundData Distillation: Towards Omni-Supervised Learning
Radosavovic, I., Dollár, P., Girshick, R., Gkioxari, G., & He, K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00433 · Cited by 446
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersPreprint
Huang, Z., Zeng, Z., Liu, B., Fu, D., & Fu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00849 · Cited by 433
ActBERT: Learning Global-Local Video-Text Representations
Zhu, L., & Yang, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00877 · Cited by 425
12-in-1: Multi-Task Vision and Language Representation Learning
Lu, J., Goswami, V., Rohrbach, M., Parikh, D., & Lee, S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01045 · Cited by 420
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.161 · Cited by 408
VirTex: Learning Visual Representations from Textual Annotations
Desai, K., & Johnson, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01101 · Cited by 376
Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning
Chen, S., Zhao, Y., Jin, Q., & Wu, Q. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01065 · Cited by 350
Xu, R., Xiong, C., Chen, W. R., & Corso, J. J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9512 · Cited by 311
Dual Encoding for Zero-Example Video Retrieval
Dong, J., Li, X., Xu, C., Ji, S., He, Y., Yang, G., & Wang, X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00957 · Cited by 300
Associating neural word embeddings with deep image representations using Fisher Vectors
Klein, B., Lev, G., Sadeh, G., & Wolf, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299073 · Cited by 292
Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections
Gong, Y., Wang, L., Hodosh, M., Hockenmaier, J., & Lazebnik, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_35 · Cited by 288
UNITER: Learning UNiversal Image-TExt Representations
Chen, Y.-C., Li, L., Yu, L., Kholy, A. E., Ahmed, F., Gan, Z., Cheng, Y., & Liu, J. (2019) · Cited by 280
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2020) · International Conference on Learning Representations · Cited by 276
A Short Note about Kinetics-600
Carreira, J., Noland, E., Banki-Horvath, A., Hillier, C., & Zisserman, A. (2018) · arXiv · doi:10.48550/arxiv.1808.01340 · Cited by 270
Self-citationSelf-Supervised MultiModal Versatile NetworksPreprint
Alayrac, J.-B., Recasens, A., Schneider, R. G., Arandjelović, R., Ramapuram, J., Fauw, J. D., Smaira, L., Dieleman, S., & Zisserman, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16228 · Cited by 258
Self-citationLearning a Text-Video Embedding from Incomplete and Heterogeneous DataPreprint
Miech, A., Laptev, I., & Šivic, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.02516 · Cited by 242
Self-citationSupport-set bottlenecks for video-text representation learningPreprint
Patrick, M., Huang, P.-Y., Asano, Y. M., Metze, F., Hauptmann, A. G., Henriques, J., & Vedaldi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02824 · Cited by 214
D3D: Distilled 3D Networks for Video Action Recognition
Stroud, J., Ross, D. A., Sun, C., Deng, J., & Sukthankar, R. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093274 · Cited by 194
Evolving Losses for Unsupervised Video Representation Learning
Piergiovanni, A., Angelova, A., & Ryoo, M. S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00021 · Cited by 140
Contrastive Learning for Weakly Supervised Phrase Grounding
Gupta, T., Vahdat, A., Chechik, G., Yang, X., Kautz, J., & Hoiem, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_44 · Cited by 125
Enhancing Video Summarization via Vision-Language Embedding
Plummer, B. A., Brown, M., & Lazebnik, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.118 · Cited by 112
Temporal Localization of Moments in Video Collections with Natural Language.Preprint
Escorcia, V. A., Soldan, M., Šivic, J., Ghanem, B., & Russell, B. C. (2019) · arXiv (Cornell University) · Cited by 65
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Geigle, G., Pfeiffer, J., Reimers, N., Vulić, I., & Gurevych, I. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00473 · Cited by 60
Attention-Based Query Expansion Learning
Gordo, A., Radenović, F., & Berg, T. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_11 · Cited by 41
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal RetrievalPreprint
Geigle, G., Pfeiffer, J., Reimers, N., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11920 · Cited by 1
Research works printed but not linked: 4
- 10.1109/ICCV.2019.00054 · DOI
- 10.1109/CVPR42600.2020.01079 · DOI · Paper page
- daniel, 2017, Thinking Fast and Slow
- chen, 2017, Learning efficient object detection models with knowledge distillation, NIPS
Cited by
Sorted by citations
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
Jiang, D., & Ye, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00273 · Cited by 367
Perceiver IO: A General Architecture for Structured Inputs & OutputsPreprint
Jaegle, A., Borgeaud, S., Alayrac, J.-B., Doersch, C., Ionescu, C., Ding, X. D., Koppula, S., Zoran, D., Brock, A., Shelhamer, E., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14795 · Cited by 641
Self-citationX-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Gorti, S. K., Vouitsis, N., Ma, J., Golestan, K., Volkovs, M., Garg, A., & Yu, G. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00495 · Cited by 261
Selva, J., Johansen, A. S., Escalera, S., Nasrollahi, K., Moeslund, T. B., & Clapés, A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3243465 · Cited by 192
Metadata
- Authors
- Miech, A., Alayrac, J.-B., Laptev, I., Sivic, J., & Zisserman, A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 9821-9831
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3141362964
- Cited by
- 149
- References
- 85
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 31 · 2023: 39 · 2022: 39 · 2021: 12
- FWCI
- 9.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Regional Development Fund
- 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
- text-to-image retrieval, video-text retrieval, cross-attention, dual-encoder models, approximate nearest neighbor search, knowledge distillation, re-ranking, inference efficiency, large-scale image retrieval, 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
