Predicting Motivations of Actions by Leveraging Text

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NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (2021, background): “Other works like Motivation [46], VCR [58] and V2C [10] may also take causality into consideration, either for visual description or QA.”
Affordances from Human Videos as a Versatile Representation for Robotics (2023, background): “Similarly, in action anticipation and human motion forecasting, high-level semantic or low level actions are predicted using visual history [1, 11,19,22,31–33,37,40,46–48,55,58,72,75,100,119,120].”
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs (2020, background): “, 2018), motivation prediction (Vondrick et al., 2016), algebraic word problems (Ling et al.”
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning (2020, background): “Work for inferring motivations of human actions in static images by incorporating commonsense knowledge are reflected in Pirsiavash et al. (2014); Vondrick et al. (2016).”
It Takes Two to Tango: Towards Theory of AI's Mind (2017, background): “research has traditionally placed much of the burden on the AI to play its part in the team: to be more accurate [28,48,56,49,32], more human-like [3,31,40,20,2,46,42,9,11], understand our intentions [41,57,58], beliefs [19], tendencies [15], contexts [47], and mental states [17,16]. In this work, w”