An Adversarial Explainable Artificial Intelligence (XAI) Based Approach for Action Forecasting

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Abstract

: Despite the growing popularity of machine learning technology, vision‐based action recognition/forecasting systems are seen as black‐boxes by the user. The effecti‐ veness of such systems depends on the machine learning algorithms, it is difficult (or impossible) to explain the de‐ cisions making processes to the users. In this context, an approach that offers the user understanding of these re‐ asoning models is significant. To do this, we present an Explainable Artificial Intelligence (XAI) based approach to action forecasting using structured database and object affordances definition. The structured database is sup‐ porting the prediction process. The method allows to vi‐ sualize the components of the structured database. Later, the components of the base are used for forecasting the nominally possible motion goals. The object affordance explicated by the probability functions supports the se‐ lection of possible motion goals. The presented methodo‐ logy allows satisfactory explanations of the reasoning be‐ hind the inference mechanism. Experimental evaluation was conducted using the WUT‐18 dataset, the efficiency of the presented solution was compared to the other ba‐ seline algorithms.