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SUMMARY:CIS Seminar: "Towards Human-Level Recognition via Contextual\, Dynamic\, and Predictive Representations"
DESCRIPTION:Abstract:\n\nExisting state-of-the-art computer vision models usually specialize in single domains or tasks\, while human-level recognition can be contextual for diverse scales and tasks. This specialization isolates different vision tasks and hinders deployment of robust and effective vision systems. In this talk\, I will discuss contextural image representations for different scales and tasks through the lens of pixel-level prediction. These connections\, built by the study of dilated convolutions and deep layer aggregation\, can interpret convolutional network behaviors and lead to model frameworks applicable to a wide range of tasks. Beyond contextual\, I will argue that image representation should also be dynamic and predictive. I will illustrate the case with input-dependent dynamic networks\, which lead to new insights into the relationship of zero-shot/few-shot learning and network pruning\, and with semantic predictive control\, which utilizes prediction for better driving policy learning. To conclude\, I will discuss the on-going system and algorithm investigations which couple representation learning and real-world interaction to build intelligent agents that can continuously learn from and interact with the world.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-towards-human-level-recognition-via-contextual-dynamic-and-predictive-representations/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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