Towards semantic, debiased and moment video retrieval - Satar, Burak (Nanyang Technological University, 2025)
Video retrieval aims to retrieve a whole video within a video corpus given a language query. However, one of the main challenges is that it requires reaching a semantic correlation between these modalities. Besides, imbalanced datasets can cause biases in the retrieval models. Moreover, retrieving a moment from a video corpus corresponding to a text query is even more challenging, especially for long egocentric videos, considering the need for fine-grained cross-modal reasoning. In this thesis, we address these problems to build machines intelligent enough towards semantic, debiased, and moment video retrieval.