Effective Utilization of Pre-Trained Models in Ad-Hoc Video Search

Author:

UEKI Kazuya1,SUZUKI Yuma2,HORI Takayuki2,TAKUSHIMA Hiroki2,OKAMOTO Hideaki2,TANOUE Hayato2

Affiliation:

1. 明星大学 情報学部 情報学科

2. ソフトバンク株式会社

Publisher

Japan Society for Precision Engineering

Subject

Mechanical Engineering

Reference27 articles.

1. 1) G. Awad, K. Curtis, A. A. Butt, J. Fiscus, A. Godil, Y. Lee, A. Delgado, J. Zhang, E. Godard, B. Chocot, L. Diduch, J. Liu, Y. Graham, G. Quénot, “An overview on the evaluated video retrieval tasks at TRECVID 2022,” In Proc. of TRECVID 2022, (2022).

2. 2) A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, T. Mikolov, “DeViSE: A Deep Visual-Semantic Embedding Model,” In Proc. of Advances in Neural Information Processing Systems (NIPS), 26, (2013).

3. 3) R. Kiros, R. Salakhutdinov, R. S. Zemel, “Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models,” In Proc. of NIPS 2014 Deep Learning Workshop, (2014).

4. 4) O. Vinyals, A. Toshev, S. Bengio, D. Erhan, “Show and Tell: A Neural Image Caption Generator,” In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015).

5. 5) A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, “Learning Transferable Visual Models From Natural Language Supervision,” arXiv:2103.00020, (2021).

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