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Found 5 papers, 2 papers with code
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time.
Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos
Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations.
Effectively Leveraging Attributes for Visual Similarity
This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.
Combining Multiple Cues for Visual Madlibs Question Answering
This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset.
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset.
