Ms. Munaza Nawaz Profile

Munaza Nawaz

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Proceedings Article | 1 May 2022 Paper

IVTEN: integration of visual-textual entities for temporal activity localization via language in video

Hafiza Sadia Nawaz, Eric Rigall, Munaza Nawaz, Israel Mugunga, Junyu Dong

Proceedings Volume 12177, 121772S (2022) https://doi.org/10.1117/12.2624682

KEYWORDS: Visualization, Video, Computer programming, Information visualization, Feature extraction, Transformers, Finite element methods, Visual process modeling, Associative arrays, Visual analytics

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This study looks into the stumbling block of temporal activity localization via natural language (TALL) in the untrimmed video. It’s a difficult task since the target temporal activity may be misled by disorder query. Existing approaches used sliding windows, regression, and ranking to handle the query without the usage of grammar-based rules. When a query is out of sequence and cannot be correlated with the relevant activity, these approaches suffer performance deterioration. We introduce the visual, action, object, and connecting words concepts to address the issue of non-sequence queries. Integration of visual-textual entities network (IVTEN) is our proposed architecture, which consists of three submodules: (1) visual graph convolutional network (visual-GCN), (2) textual graph convolutional network (textual-GCN), and (3) compatible method for learning embeddings (CME). Visual nodes detect activity, object, and actor in the same way as textual nodes maintain word sequence using grammar-based rules. (CME) integrates several modalities (activity, query) and trained grammar-based words into the same embedding space. We also include a stochastic latent variable in CME to align and retain the query sequence with the relevant activity. On three typical benchmark datasets, our IVTEN approach outperforms the state-of-the-art Charades-STA, TACoS, and ActivityNet-Captions.

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