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GeezerVison is showing mvcs-workshop.github.io — CVPR2023 MVCS. Machine Visual Common Sense: Perception, Prediction, Planning The Workshop on Machine Visual Common Sense: Perception, Prediction, PlanningJune 19 @ AboutOver the years, there have been a variety of visual reasoning tasks that evaluate machines’ ability to understand and reason about visual scenes. However, these benchmarks mostly focus on classification of objects and items that exist in a scene. Common sense reasoning – an understanding of what might happen next, or what gave rise to the scene – is often absent in these benchmarks. Humans, on the other hand, are highly versatile, adept in numerous high-level cognition-related visual reasoning tasks that go beyond pattern recognition and require common sense (e.g., physics, causality, functionality, psychology, etc). In order to design systems with human-like visual understanding of the world, we would like to emphasize benchmarks and tasks that evaluate common sense reasoning across a variety of domains, including but not limited to:
Challenge TracksThere will be two core tracks in the machine vision common sense challenge: STAR ChallengeSTAR (accepted by NeurIPS2021). STAR Benchmark is a novel dataset for Situated Reasoning, which provides challenging question-answering tasks, symbolic situation descriptions and logic-grounded diagnosis via real-world video situations. Reasoning in the real world is not divorced from situations. A key challenge is to capture the present knowledge from surrounding situations and reason accordingly. Situated reasoning is a cognitive process where a person uses context and environmental cues to make decisions and solve problems. CLEVRER & ComPhy ChallengeCLEVRER (accepted by ICLR2020) and ComPhy (accepted by ICLR 2022). CLEVRER is a diagnostic video dataset for systematic evaluation of computational models on a wide range of reasoning tasks. Motivated by the theory of human casual judgment, CLEVRER includes four types of question: descriptive (e.g., “what color"), explanatory (”what’s responsible for"), predictive (”what will happen next"), and counterfactual (“what if"). ComPhy takes a step further and requires machines to learn the new compositional visible and hidden physical properties from only a few examples. ComPhy includes three types of questions: factual questions for the composition between visible and hidden physical properties, counterfactual questions on objects’ physical properties like mass and charge, and predictive questions for objects’ future movement. Download Link (CLEVRER) Invited Speakers
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