Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
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We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.
Out of the three subtasks, this paper reports the system description of subtask A and subtask B.
Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.
A significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades.
The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.
Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.
Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.