Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization
This addresses the problem of limited annotated data and semantic complexity in video understanding for computer vision researchers, though it is incremental as it builds on existing knowledge base integration methods.
The paper tackled the problem of video activity interpretation by constructing deep semantic representations through reasoning about relationships among concepts, using an energy minimization framework that leverages commonsense knowledge from ConceptNet to reduce reliance on large annotated datasets. The result showed that the proposed model generates better-quality video interpretations than state-of-the-art approaches on three public datasets, handling challenges like data imbalance and complex scenes.
A deeper understanding of video activities extends beyond recognition of underlying concepts such as actions and objects: constructing deep semantic representations requires reasoning about the semantic relationships among these concepts, often beyond what is directly observed in the data. To this end, we propose an energy minimization framework that leverages large-scale commonsense knowledge bases, such as ConceptNet, to provide contextual cues to establish semantic relationships among entities directly hypothesized from video signal. We mathematically express this using the language of Grenander's canonical pattern generator theory. We show that the use of prior encoded commonsense knowledge alleviate the need for large annotated training datasets and help tackle imbalance in training through prior knowledge. Using three different publicly available datasets - Charades, Microsoft Visual Description Corpus and Breakfast Actions datasets, we show that the proposed model can generate video interpretations whose quality is better than those reported by state-of-the-art approaches, which have substantial training needs. Through extensive experiments, we show that the use of commonsense knowledge from ConceptNet allows the proposed approach to handle various challenges such as training data imbalance, weak features, and complex semantic relationships and visual scenes.