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Found 3 papers, 1 papers with code
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Our results show that the selective relevance method can not only provide insight on the role played by motion in the model's decision -- in effect, revealing and quantifying the model's spatial bias -- but the method also simplifies the resulting explanations for human consumption.
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations
This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for video deep learning, and then noting the scarcity of research into explanations for these methods.
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition
However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks.
