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Lifestyle, Americas, Special Report - Aaaazcccz - 06-10-2022
It's simple probability. Urban areas - even the sprawling ones - make up just a small fraction of the total land area. Chances are slim that any tornado will hit one of these densely populated places, much less the most powerful tornadoes. But it has happened in the past. Will again in the future. Don't believe everything you see in the movies; hiding under an overpass is a terrible idea. It's understandable why motorists would seek out underpasses when a tornado threatens. There's not much else that even remotely resembles shelter when you're driving across the rural plains. But the meteorologists have spoken: Don't do it! This tornado survival myth likely started in 1991 when a television news crew, with cameras rolling, took cover under an overpass during a very weak tornado. They survived, and the resulting footage was widely circulated. It created a surprisingly persistent misconception that was still alive. Well in 2013 when the movie "Man of Steel" depicted Clark Kent's dad taking shelter from a tornado under - you guessed it - an overpass.|In this paper, we propose a framework named OCSampler to explore a compact yet effective video representation with one short clip for efficient video recognition. Recent works prefer to formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance, while we present a new paradigm of learning instance-specific video condensation policies to select informative frames for representing the entire video only in a single step. Our basic motivation is that the efficient video recognition task lies in processing a whole sequence at once rather than picking up frames sequentially. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network within one step. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high con?dence with as few frames as possible. Experiments on four benchmarks, i.e., ActivityNet, Mini-Kinetics, FCVID, Mini-Sports1M, demonstrate the effectiveness of our OCSampler over previous methods in terms of accuracy, theoretical computational expense, actual inference speed.
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