Learning Aligned Cross-Modal Representations from Weakly Aligned Data

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

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 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

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 · see citing works

Curriculum learning

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works

Learning Transferable Features with Deep Adaptation Networks

Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505 · see citing works

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works

Canonical Correlation Analysis: An Overview with Application to Learning Methods

Hardoon, D. R., Szedmák, S., & Shawe-Taylor, J. S. (2004) · Neural Computation · doi:10.1162/0899766042321814 · Cited by 3,297 · see citing works

SUN database: Large-scale scene recognition from abbey to zoo

Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283 · see citing works

One-shot learning of object categories

Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works

Adapting visual category models to new domains

Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847 · see citing works

Untitled work

(2011) · Cited by 2,752 · see citing works

Untitled work

(2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2,534 · see citing works

Unbiased look at dataset bias

Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works

DeViSE: A Deep Visual-Semantic Embedding Model

Frome, A., Corrado, G. S., Shlens, J., Samy Bengio, A., Dean, J., Ranzato, M., & Mikolov, T. (2013) · Cited by 2,309 · see citing works

Signature Verification Using A "Siamese" Time Delay Neural Network

BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077 · see citing works

Learning to detect unseen object classes by between-class attribute transfer

Lampert, C. H., Nickisch, H., & Harmeling, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206594 · Cited by 1,849 · see citing works

Learning to detect unseen object classes by between-class attribute transfer

Lampert, C. H., Nickisch, H., & Stefan Harmeling, B. (2009) · 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/cvprw.2009.5206594 · Cited by 1,563 · see citing works

Unifying Visual-Semantic Embeddings with Multimodal Neural Language ModelsPreprint

Kiros, R., Salakhutdinov, R., & Zemel, R. S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.2539 · Cited by 1,386 · see citing works

A new approach to cross-modal multimedia retrieval

Rasiwasia, N., Pereira, J. C., Coviello, E., Doyle, G., Lanckriet, G. R. G., Lévy, R., & Vasconcelos, N. M. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1873987 · Cited by 1,358 · see citing works

Skip-Thought Vectors

Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293 · see citing works

Simultaneous Deep Transfer Across Domains and Tasks

Tzeng, E., Hoffman, J., Darrell, T., & Saenko, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.463 · Cited by 1,203 · see citing works

Domain adaptation for object recognition: An unsupervised approach

Gopalan, R., Li, R., & Chellappa, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126344 · Cited by 1,059 · see citing works

Zero-Shot Learning Through Cross-Modal Transfer

Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C. D., & Ng, A. Y. (2013) · arXiv · doi:10.48550/arxiv.1301.3666 · Cited by 997 · see citing works

Zero-Shot Learning by Convex Combination of Semantic EmbeddingsPreprint

Norouzi, M., Mikolov, T., Samy Bengio, A., Singer, Y., Shlens, J., Frome, A., Corrado, G. S., & Dean, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5650 · Cited by 890 · see citing works

Object Detectors Emerge in Deep Scene CNNsPreprint

Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6856 · Cited by 823 · see citing works

Zero-Shot Learning with Semantic Output Codes

Palatucci, M., Pomerleau, D., Hinton, G. E., & Mitchell, T. M. (2018) · Figshare · doi:10.1184/r1/6476456 · Cited by 752 · see citing works

Geometric context from a single image

Hoiem, D., Efros, A. A., & Hebert, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.107 · Cited by 666 · see citing works

Undoing the Damage of Dataset Bias

Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_12 · Cited by 482 · see citing works

Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors

Eitz, M., Hildebrand, K., Boubekeur, T., & Alexa, M. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.266 · Cited by 411 · see citing works

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions

Ba, J. L., Swersky, K., Fidler, S., & Salakhutdinov, R. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.483 · Cited by 401 · see citing works

Visually Indicated Sounds

Owens, A., Isola, P., McDermott, J. H., Torralba, A. B., Adelson, E. H., & Freeman, W. T. (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.2016.264 · Cited by 352 · see citing works

Contextual models for object detection using boosted random fields

Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 303 · see citing works

HOGgles: Visualizing Object Detection Features

Vondrick, C., Khosla, A., Malisiewicz, T., & Torralba, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.8 · Cited by 282 · see citing works

Sketch-based 3D shape retrieval using Convolutional Neural Networks

Wang, F., Kang, L., & Li, Y. (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.2015.7298797 · Cited by 275 · see citing works

Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions

Elhoseiny, M., Saleh, B., & Elgammal, A. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.321 · Cited by 263 · see citing works

Multi-label Cross-Modal Retrieval

Ranjan, V., Rasiwasia, N., & Jawahar, C. V. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.466 · Cited by 244 · see citing works

Object Classification from a Single Example Utilizing Class Relevance Metrics

Fink, M. (2004) · Cited by 206 · see citing works

Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement

Bart, E., & Ullman, S. (2005) · 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.2005.117 · Cited by 202 · see citing works

Bringing Semantics into Focus Using Visual Abstraction

Zitnick, C. L., & Parikh, D. (2013) · 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.2013.387 · Cited by 197 · see citing works

Learning cross-modality similarity for multinomial data

Jia, Y., Salzmann, M., & Darrell, T. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126524 · Cited by 180 · see citing works

Learning the Visual Interpretation of Sentences

Zitnick, C. L., Parikh, D., & Vanderwende, L. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.211 · Cited by 156 · see citing works

Cluster Canonical Correlation Analysis

Rasiwasia, N., Mahajan, D. K., Mahadevan, V., & Aggarwal, G. (2014) · Cited by 152 · see citing works

Inverting Convolutional Networks with Convolutional Networks.Preprint

Dosovitskiy, A., & Brox, T. (2015) · arXiv (Cornell University) · Cited by 75 · see citing works

Predicting Object Dynamics in Scenes

Fouhey, D. F., & Zitnick, C. L. (2014) · 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.2014.260 · Cited by 74 · see citing works

What Makes a Good Detector? – Structured Priors for Learning from Few Examples

Gao, T., Stark, M. M., & Koller, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_26 · Cited by 25 · see citing works

Learning visual biases from human imaginationPreprint

Vondrick, C., Pirsiavash, H., Oliva, A., & Torralba, A. B. (2014) · arXiv (Cornell University) · doi:10.13016/m2w3st-cf3r · Cited by 14 · see citing works

Part level transfer regularization for enhancing exemplar SVMs

Aytar, Y., & Zisserman, A. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.04.004 · Cited by 9 · see citing works

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., & Bengio, Y. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.156 · Cited by 1,762 · see citing works

Through-Wall Human Pose Estimation Using Radio Signals

Zhao, M., Li, T., Alsheikh, M. A., Tian, Y., Zhao, H., Torralba, A. B., & Katabi, D. (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.2018.00768 · Cited by 676 · see citing works

SoundNet: Learning Sound Representations from Unlabeled VideoPreprint

Aytar, Y., Vondrick, C., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09001 · Cited by 658 · see citing works

Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation

Inoue, N., Furuta, R., Yamasaki, T., & Aizawa, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00525 · Cited by 616 · see citing works

Dual-path Convolutional Image-Text Embeddings with Instance Loss

Zheng, Z., Zheng, L., Garrett, M., Yang, Y., Xu, M., & Shen, Y.-D. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3383184 · Cited by 622 · see citing works

Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

Salvador, A., Hynes, N., Aytar, Y., Marín, J., Ofli, F., Weber, I. G., & Torralba, A. B. (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.2017.327 · Cited by 416 · see citing works

Handling incomplete heterogeneous data using VAEs

Nazábal, A., Olmos, P. M., Zoubin Ghahramani, A., & Valera, I. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107501 · Cited by 369 · see citing works

Multimodal MR Synthesis via Modality-Invariant Latent Representation

Chartsias, A., Joyce, T., Giuffrida, M. V., & Tsaftaris, S. A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2764326 · Cited by 343 · see citing works

An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges

Peng, Y., Huang, X., & Zhao, Y. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2705068 · Cited by 342 · see citing works

Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

Marín, J., Biswas, A., Ofli, F., Hynes, N., Salvador, A., Aytar, Y., Weber, I. G., & Torralba, A. B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2927476 · Cited by 350 · see citing works

Context Autoencoder for Self-supervised Representation Learning

Chen, X., Ding, M., Wang, X., Xin, Y., Mo, S., Wang, Y., Han, S., Luo, P., Zeng, G., & Wang, J. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01852-4 · Cited by 330 · see citing works

Domain Adaptation for Visual Applications: A Comprehensive SurveyPreprint

Csurka, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05374 · Cited by 324 · see citing works

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis

Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., & Mougiakakou, S. G. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636929 · Cited by 296 · see citing works

Deep Partial Multi-View Learning

Zhang, C., Cui, Y., Han, Z., Zhou, J. T., Fu, H., & Hu, Q. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3037734 · Cited by 282 · see citing works

Transfer Learning

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773 · Cited by 272 · see citing works

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268 · see citing works

Colloquium : Machine learning in nuclear physics

Boehnlein, A., Diefenthaler, M., Sato, N., Schram, M., Ziegler, V., Fanelli, C., Hjorth-Jensen, M., Horn, T., Kuchera, M., Lee, D., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.031003 · Cited by 263 · see citing works

Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges

Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227 · see citing works

Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Bakkouri, I., & Afdel, K. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07988-1 · Cited by 208 · see citing works

Omnivore: A Single Model for Many Visual Modalities

Girdhar, R., Singh, M., Ravi, N., van der Maaten, L., Joulin, A., & Misra, I. (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/cvpr52688.2022.01563 · Cited by 212 · see citing works

Transitive Transfer Learning

Tan, B., Song, Y., Zhong, E., & Yang, Q. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783295 · Cited by 198 · see citing works

RGB-IR Person Re-identification by Cross-Modality Similarity Preservation

Wu, A., Zheng, W., Gong, S., & Lai, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01290-1 · Cited by 172 · see citing works

Speech2Face: Learning the Face Behind a Voice

Oh, T.-H., Dekel, T., Kim, C., Mosseri, I., Freeman, W. T., Rubinstein, M., & Matusik, W. (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.2019.00772 · Cited by 167 · see citing works

Automatic Understanding of Image and Video Advertisements

Hussain, Z., Zhang, M., Zhang, X., Ye, K., Thomas, C., Agha, Z., Ong, N., & Kovashka, A. (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.2017.123 · Cited by 160 · see citing works

Label Efficient Learning of Transferable Representations across Domains and TasksPreprint

Luo, Z., Zou, Y., Hoffman, J., & Fei-Fei, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00123 · Cited by 172 · see citing works

Deep Learning for Free-Hand Sketch: A Survey

Xu, P., Hospedales, T. M., Yin, Q., Song, Y.-Z., Xiang, T., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148853 · Cited by 180 · see citing works

Self-Supervised Moving Vehicle Tracking With Stereo Sound

Gan, C., Zhao, H., Chen, P., Cox, D., & Torralba, A. B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00715 · Cited by 150 · see citing works

Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations

Venkateswara, H., Chakraborty, S., & Panchanathan, S. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2740460 · Cited by 124 · see citing works

A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification

Song, L., Liu, J., Qian, B., Sun, M., Yang, K., Sun, M., & Abbas, S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2864920 · Cited by 116 · see citing works

CPM-Nets: Cross Partial Multi-View Networks

Zhang, C., Han, Z., Cui, Y., Fu, H., Zhou, J. T., & Hu, Q. (2019) · neural information processing systems · Cited by 114 · see citing works

See, Hear, and Read: Deep Aligned RepresentationsPreprint

Aytar, Y., Vondrick, C., & Torralba, A. B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00932 · Cited by 117 · see citing works

Reasoning on the Relation: Enhancing Visual Representation for Visual Question Answering and Cross-Modal Retrieval

Yu, J., Zhang, W., Lu, Y., Qin, Z., Hu, Y., Tan, J., & Wu, Q. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2972830 · Cited by 96 · see citing works

2022 Review of Data-Driven Plasma Science

Anirudh, R., Archibald, R. K., Asif, M. S., Becker, M. M., Benkadda, S., Bremer, P., Budé, R. H. S., Chang, C. S., Chen, L., Churchill, R. M., et al. (2023) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2023.3268170 · Cited by 97 · see citing works

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition

Zhang, J., Li, W., Ogunbona, P., & Xu, D. (2019) · ACM Computing Surveys · doi:10.1145/3291124 · Cited by 127 · see citing works

Transfer Learning in Natural Language Processing

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.020 · Cited by 94 · see citing works

WildFish

Zhuang, P., Wang, Y., & Qiao, Y. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240616 · Cited by 84 · see citing works

OmniMAE: Single Model Masked Pretraining on Images and Videos

Girdhar, R., El-Nouby, A., Singh, M., Alwala, K. V., Joulin, A., & Misra, I. (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/cvpr52729.2023.01003 · Cited by 78 · see citing works

Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation

Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, É., & Pérez, P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3159589 · Cited by 75 · see citing works

Attention-Based Autism Spectrum Disorder Screening With Privileged Modality

Chen, S., & Zhao, Q. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00127 · Cited by 72 · see citing works

Visually Grounded Neural Syntax Acquisition

Shi, H., Mao, J., Gimpel, K., & Livescu, K. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1180 · Cited by 68 · see citing works

Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features

Horiguchi, S., Ikami, D., & Aizawa, K. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2911075 · Cited by 65 · see citing works

Sensor modality fusion with CNNs for UGV autonomous driving in indoor environments

Patel, N., Choromanska, A., Krishnamurthy, P., & Khorrami, F. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205958 · Cited by 59 · see citing works

Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns

Shen, Y., Pokharel, R., Nizolek, T. J., Kumar, A., & Lookman, T. (2019) · Acta Materialia · doi:10.1016/j.actamat.2019.03.026 · Cited by 55 · see citing works

Deep Visual Domain Adaptation

Csurka, G. (2020) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc51798.2020.00013 · Cited by 66 · see citing works

AutoFoley: Artificial Synthesis of Synchronized Sound Tracks for Silent Videos With Deep Learning

Ghose, S., & Prevost, J. J. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3005033 · Cited by 51 · see citing works

Semantic analysis on social networks: A survey

Bayrakdar, S., Yücedağ, İ., Şimşek, M., & Doğru, İ. A. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4424 · Cited by 51 · see citing works

Towards smart optical focusing: deep learning-empowered dynamic wavefront shaping through nonstationary scattering media

Luo, Y., Yan, S., Li, H., Lai, P., & Zheng, Y. (2021) · Photonics Research · doi:10.1364/prj.415590 · Cited by 48 · see citing works

Face-Voice Matching using Cross-modal Embeddings

Horiguchi, S., Kanda, N., & Nagamatsu, K. (2018) · ACM Multimedia · doi:10.1145/3240508.3240601 · Cited by 59 · see citing works

Deep Memory Network for Cross-Modal Retrieval

Song, G., Wang, D., & Tan, X. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2877122 · Cited by 44 · see citing works

A Benchmark Dataset and Learning High-Level Semantic Embeddings of Multimedia for Cross-Media Retrieval

Rehman, S. U., Tu, S., Huang, Y., & Rehman, O. U. (2018) · IEEE Access · doi:10.1109/access.2018.2878868 · Cited by 44 · see citing works

Social network search based on semantic analysis and learning

Kou, F., Du, J., He, Y., & Ye, L. (2016) · CAAI Transactions on Intelligence Technology · doi:10.1016/j.trit.2016.12.001 · Cited by 43 · see citing works

Recognition From Web Data: A Progressive Filtering Approach

Yang, J., Sun, X., Lai, Y., Zheng, L., & Cheng, M. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2855449 · Cited by 40 · see citing works

Improving Night-Time Pedestrian Retrieval With Distribution Alignment and Contextual Distance

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SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches

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Through-Wall Human Pose Estimation by Mutual Information Maximizing Deeply Supervised Nets

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Correction to: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

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CREAMY: Cross-Modal Recipe Retrieval By Avoiding Matching Imperfectly

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Audio-to-Visual Cross-Modal Generation of Birds

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Exploiting Convolution Filter Patterns for Transfer Learning

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Mix and Match Networks: Cross-Modal Alignment for Zero-Pair Image-to-Image Translation

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Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory

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Cross Attention with Transformer for Few-shot Medical Image Segmentation

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Discriminative semantic transitive consistency for cross-modal learning

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