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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Visual search of relevant targets in the environment is a crucial robot skill.
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We integrate two powerful ideas, geometry and deep visual representation learning, into recurrent network architectures for mobile visual scene understanding.
COMMON SENSE REASONING REPRESENTATION LEARNING SCENE UNDERSTANDING
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In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem.
COMMON SENSE REASONING MACHINE READING COMPREHENSION QUESTION ANSWERING VISUAL QUESTION ANSWERING
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Our model is shown to be effective in detecting anomalies in videos.
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Often missing in existing knowledge bases of facts, are relationships that encode common sense knowledge about unnamed entities.
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The NLP and ML communities have long been interested in developing models capable of common-sense reasoning, and recent works have significantly improved the state of the art on benchmarks like the Winograd Schema Challenge (WSC).
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We introduce a new benchmark task for coreference resolution, Hard-CoRe, that targets common-sense reasoning and world knowledge.
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When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context.
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Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized.