Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization

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.Comment: Accepted to WACV 201

The scene superiority effect: object recognition in the context of natural scenes

Four experiments investigate the effect of background scene semantics on object recognition. Although past research has found that semantically consistent scene backgrounds can facilitate recognition of a target object, these claims have been challenged as the result of post-perceptual response bias rather than the perceptual processes of object recognition itself. The current study takes advantage of a paradigm from linguistic processing known as the Word Superiority Effect. Humans can better discriminate letters (e.g., D vs. K) in the context of a word (WORD vs. WORK) than in a non-word context (e.g., WROD vs. WROK) even when the context is non-predictive of the target identity. We apply this paradigm to objects in natural scenes, having subjects discriminate between objects in the context of scenes. Because the target objects were equally semantically consistent with any given scene and could appear in either semantically consistent or inconsistent contexts with equal probability, response bias could not lead to an apparent improvement in object recognition. The current study found a benefit to object recognition from semantically consistent backgrounds, and the effect appeared to be modulated by awareness of background scene semantics