Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search

Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search
Title:
Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search
Other Titles:
2019 IEEE International Conference on Image Processing (ICIP)
Publication Date:
22 September 2019
Citation:
Z. Chen, J. Lin, Z. Wang, V. Chandrasekhar and W. Lin, "Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search," 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 879-883. doi: 10.1109/ICIP.2019.8802944
Abstract:
In this paper, we propose Deep Holographic Networks (DHN) to learn similarity metrics of videos for multi-label video search. DHN introduces a holographic composition layer to explicitly encode similarity metrics at intermediate layer of the network, instead of conventional deep metric learning approaches driven by ranking losses. The holographic composition layer is parameter-free and enables less memory footprint, compared with state-of-the-art. Towards multi-label video search at large scale, we present a new video benchmark built upon the YouTube-8M dataset. Extensive evaluations on this dataset demonstrate that DHN performs better than traditional deep metric learning approaches as well as other compositional networks.
License type:
PublisherCopyrights
Funding Info:
NA
Description:
(C) 2019 IEEE.
ISSN:
2381-8549
1522-4880
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