no code implementations • 13 Oct 2021 • Haider Al-Tahan, Yalda Mohsenzadeh
Hence, we extensively investigate composition of temporal augmentations suitable for learning audiovisual representations; we find lossy spatio-temporal transformations that do not corrupt the temporal coherency of videos are the most effective.
no code implementations • 19 Oct 2020 • Haider Al-Tahan, Yalda Mohsenzadeh
We illustrate that by combining all these methods and with substantially less labeled data, our framework (CLAR) achieves significant improvement on prediction performance compared to supervised approach.