In the intricate problem of understanding long-form multi-modal inputs, few key-aspects in scene-understanding and dialogue-and-discourse are often overlooked. In this paper, we investigate two such key-aspects for better semantic and relational understanding - (i). head-object-tracking in addition to usual face-tracking, and (ii). fusing scene-to-text representation with external common-sense knowledge-base for effective mapping to sub-tasks of interest. The usage of head-tracking especially helps with enriching sparse entity mapping to inter-entity conversation interactions. These methods are guided by natural language supervision on visual models, and perform well for interaction and sentiment understanding tasks.

The natural language processing community has had a major interest in auto-regressive [4, 13] and span-prediction based language models [7] recently, while knowledge graphs are often referenced for common-sense based reasoning and fact-checking models. In this paper, we present an equivalence representation of span-prediction based language models and knowledge-graphs to better leverage recent developments of language modelling for multi-modal problem statements. Our method performed well, especially with sentiment understanding for multi-modal inputs, and discovered potential bias in naturally occurring videos when compared with movie-data interaction-understanding. We also release a dataset of an auto-generated questionnaire with ground-truths consisting of labels spanning across 120 relationships, 99 sentiments, and 116 interactions, among other labels for finer-grained analysis of model comparisons in the community.

We consider the problem of building semantic relationship of unseen entities from free-form multi-modal sources. This intelligent agent understands semantic properties by creating (1) logical segments from sources, (2) finds interacting objects, (3) infers their interaction actions using (4) extracted textual, auditory, visual, and tonal information. The conversational dialogue discourses are automatically mapped to interacting co-located objects, and fused with their Kinetic action embeddings at each scene of occurrence. This generates a combined probability distribution representation for interacting entities spanning over every semantic relation class. Using these probabilities, we create knowledge graphs capable of answering semantic queries and infer missing properties in a given context.