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Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities.
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F. Kreß, A. Serdyuk, T. Hotfilter, J. Hoefer, T. Harbaum, J. Becker, and T. Hamann, “Hardware-aware Workload Distribution for AI-based Online Handwriting Recognition in a Sensor Pen,” in Mediterranean Conference on Embedded Computing (MECO) , Budva, Montenegro, Jun. 2022
2022
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2022
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2022
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2022
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X. Gu, L. Ou, D. Ong, and Y. Wang, “MM-ALT: A Multimodal Automatic Lyric Transcription System,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 3328–3337
2022
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J. Wang, T. Gong, Z. Zeng, C. Sun, and Y. Yan, “C 3 CMR: Cross-Modality Cross-Instance Contrastive Learning for Cross-Media Retrieval,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 4300–4308
2022
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Y. Ohishi, M. Delcroix, T. Ochiai, S. Araki, D. Takeuchi, D. Niizumi, A. Kimura, N. Harada, and K. Kashino, “ConceptBeam: Concept Driven Target Speech Extraction,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 4252–4260
2022
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C. Luo, Y. Zhu, L. Jin, Z. Li, and D. Peng, “SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text,” in IEEE Trans. on Neural Networks and Learning Systems (TNNLS) , Feb. 2022, pp. 1–13
2022
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A. Guzhov, F. Raue, J. Hees, and A. Dengel, “AudioCLIP: Extending Clip to Image, Text and Audio,” in IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) , Singapore, Singapore, May 2022
2022
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A. Baevski, W.-N. Hsu, Q. Xu, A. Babu, J. Gu, and M. Auli, “data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language,” in Intl. Conf. on Machine Learning (ICML) , vol. 162, 2022, pp. 1298–1312
2022
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A. Piergiovanni, A. Angelova, and M. S. Ryoo, “Evolving Losses for Unsupervised Video Representation Learning,” in IEEE/CVF Intl. Conf. on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, Jun. 2022
2022
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H. Lin, Z. Ma, X. Hong, Y. Wang, and Z. Su, “Semi-supervised Crowd Counting via Density Agency,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 1416–1426
2022
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A. Singh, R. Hu, V. Goswami, G. Couairon, W. Galuba, M. Rohrbach, and D. Kiela, “FLAVA: A Foundational Language and Vision Alignment Model,” in IEEE/CVF Intl. Conf. on Computer Vision and Pattern Recognition (CVPR) , New Orleans, LA, Jun. 2022, pp. 15 638–15 650
2022
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A. Falcon, G. Serra, and O. Lanz, “A Feature-space Multimodal Data Augmentation Technique for Text-Video Retrieval,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 4385–4394
2022
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D. Chen, M. Wang, H. Chen, L. Wu, J. Qin, and W. Peng, “Cross-Modal Retrieval with Heterogeneous Graph Embedding,” in Proc. of the ACM Intl. Conf. on Multimedia (ACMMM) , Oct. 2022, pp. 3291–3300
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S. K. Singh and A. Chaturvedi, “Leveraging Deep Feature Learning for Wearable Sensors Based Handwritten Character Recognition,” in Biomedical Signal Processing and Control , vol. 80(1), Feb. 2023
2023
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J. Gan, W. Wang, J. Leng, and X. Gao, “HiGAN+: Handwriting Imitation GAN with Disentangled Representations,” in ACM Trans. on Graphics (TOG) , Feb. 2023, pp. 1–17
2023
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V. Vapnik and R. Izmailov, “Learning Using Privileged Information: Similarity Control and Knowledge Transfer,” in Journal of Machine Learning Research (JMLR) , Sep. 2015, pp. 2023–2049
2049
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