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Visual Commonsense Reasoning (VCR)

Large-scale dataset for cognition-level visual understanding

Visual Commonsense Reasoning (VCR)

Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer. Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and high-quality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (~45%).

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University of Washington
https://www.washington.edu
Task
Visual Question Answering
Annotation Types
Semantic Segmentation
110000
Items
Classes
110000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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