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.