Uncertainty-aware Unsupervised Video Hashing

Yucheng Wang, Mingyuan Zhou, Yu Sun, Xiaoning Qian
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6722-6740, 2023.

Abstract

Learning to hash has become popular for video retrieval due to its fast speed and low storage consumption. Previous efforts formulate video hashing as training a binary auto-encoder, for which noncontinuous latent representations are optimized by the biased straight-through (ST) back-propagation heuristic. We propose to formulate video hashing as learning a discrete variational auto-encoder with the factorized Bernoulli latent distribution, termed as Bernoulli variational auto-encoder (BerVAE). The corresponding evidence lower bound (ELBO) in our BerVAE implementation leads to closed-form gradient expression, which can be applied to achieve principled training along with some other unbiased gradient estimators. BerVAE enables uncertainty-aware video hashing by predicting the probability distribution of video hash code-words, thus providing reliable uncertainty quantification. Experiments on both simulated and real-world large-scale video data demonstrate that our BerVAE trained with unbiased gradient estimators can achieve the state-of-the-art retrieval performance. Furthermore, we show that quantified uncertainty is highly correlated to video retrieval performance, which can be leveraged to further improve the retrieval accuracy. Our code is available at https://github.com/wangyucheng1234/BerVAE

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-wang23i, title = {Uncertainty-aware Unsupervised Video Hashing}, author = {Wang, Yucheng and Zhou, Mingyuan and Sun, Yu and Qian, Xiaoning}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {6722--6740}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/wang23i/wang23i.pdf}, url = {https://proceedings.mlr.press/v206/wang23i.html}, abstract = {Learning to hash has become popular for video retrieval due to its fast speed and low storage consumption. Previous efforts formulate video hashing as training a binary auto-encoder, for which noncontinuous latent representations are optimized by the biased straight-through (ST) back-propagation heuristic. We propose to formulate video hashing as learning a discrete variational auto-encoder with the factorized Bernoulli latent distribution, termed as Bernoulli variational auto-encoder (BerVAE). The corresponding evidence lower bound (ELBO) in our BerVAE implementation leads to closed-form gradient expression, which can be applied to achieve principled training along with some other unbiased gradient estimators. BerVAE enables uncertainty-aware video hashing by predicting the probability distribution of video hash code-words, thus providing reliable uncertainty quantification. Experiments on both simulated and real-world large-scale video data demonstrate that our BerVAE trained with unbiased gradient estimators can achieve the state-of-the-art retrieval performance. Furthermore, we show that quantified uncertainty is highly correlated to video retrieval performance, which can be leveraged to further improve the retrieval accuracy. Our code is available at https://github.com/wangyucheng1234/BerVAE} }
Endnote
%0 Conference Paper %T Uncertainty-aware Unsupervised Video Hashing %A Yucheng Wang %A Mingyuan Zhou %A Yu Sun %A Xiaoning Qian %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-wang23i %I PMLR %P 6722--6740 %U https://proceedings.mlr.press/v206/wang23i.html %V 206 %X Learning to hash has become popular for video retrieval due to its fast speed and low storage consumption. Previous efforts formulate video hashing as training a binary auto-encoder, for which noncontinuous latent representations are optimized by the biased straight-through (ST) back-propagation heuristic. We propose to formulate video hashing as learning a discrete variational auto-encoder with the factorized Bernoulli latent distribution, termed as Bernoulli variational auto-encoder (BerVAE). The corresponding evidence lower bound (ELBO) in our BerVAE implementation leads to closed-form gradient expression, which can be applied to achieve principled training along with some other unbiased gradient estimators. BerVAE enables uncertainty-aware video hashing by predicting the probability distribution of video hash code-words, thus providing reliable uncertainty quantification. Experiments on both simulated and real-world large-scale video data demonstrate that our BerVAE trained with unbiased gradient estimators can achieve the state-of-the-art retrieval performance. Furthermore, we show that quantified uncertainty is highly correlated to video retrieval performance, which can be leveraged to further improve the retrieval accuracy. Our code is available at https://github.com/wangyucheng1234/BerVAE
APA
Wang, Y., Zhou, M., Sun, Y. & Qian, X.. (2023). Uncertainty-aware Unsupervised Video Hashing. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:6722-6740 Available from https://proceedings.mlr.press/v206/wang23i.html.

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