World Stocks

By comparison, jackhammers (sometimes called pneumatic drills or demolition hammers) make quick work of even really hard surfaces like highways, patios and rock walls. That doesn't mean jackhammers are easy to use. They tend to be unwieldy, with some weighing nearly 100 pounds (45 kilograms), meaning that safe operation requires a user with some muscle. In spite of the challenges of using jackhammers, these tools really are necessary for all sorts of major projects. And even though they perform a straightforward and brutal task, modern jackhammers are thoroughly evolved and refined machines with a long history. Keep reading and you'll see just how jackhammers came to be. Machines and power tools of all kinds made labor easier and less physically taxing. But men working in mines and quarries still had to work with pickaxes and shovels, cussing their way through the day. Brawn just wasn't enough for these exhausting jobs. What these laborers really needed was a more powerful way to break through rock to reveal minerals or release stones for construction.
However, the above work simply regarded a few frames of the target person as a batch of individual images without temporal correlations. Specifically, Fs-vid2vid extracted the appearance features of each frame of the target person individually and aggregated them with an attention mechanism. MetaPix optimized the performance of each frame in parallel using meta-learning. To address the above problems, this work proposes MetaDance for few-shot dancing video retargeting which synthesizes photo-realistic and temporally coherent videos for unseen persons with only a few frames. Instead of regarding a few frames of a target person as a batch of images without temporal correlations as in previous work, we model these frames as a few dancing moves, where each dancing move contains two consecutive frames, to capture not only the appearance patterns but also the temporal dynamics of the person. We further formulate the synthesis of a dancing video as the synthesis of a series of dancing moves, so that a video is synthesized in a move-by-move manner.
First, gravity is a force that causes objects to attract one another. The simplest way to understand gravity is through Isaac Newton's law of universal gravitation. This law states that every particle in the universe attracts every other particle. The more massive an object is, the more strongly it attracts other objects. The closer objects are, the more strongly they attract each other. An enormous object, like the Earth, easily attracts objects that are close to it, like apples hanging from trees. Scientists haven't decided exactly what causes this attraction, but they believe it exists everywhere in the universe. Second, air is a fluid that behaves essentially the same way liquids do. Like liquids, air is made of microscopic particles that move in relation to one another. Air also moves like water does -- in fact, some aerodynamic tests take place underwater instead of in the air. The particles in gasses, like the ones that make up air, are simply farther apart and move faster than the particles in liquids.
Participate in them enthusiastically. Almost every high school and college team, as well as some professional teams, has a fight song played during timeouts or after touchdowns and other big plays. Learn the words to this song, as well as any choreography that goes with it. If you're cheering for a school you attended, learn the alma mater as well. Supporting your team involves more than just singing; fans often participate in cheers or other spectacles during football games. Nowhere are these traditions more effective than at college stadiums, where tens of thousands of fans may yell and move in unison in an effort to get their team fired up. A great example of such a custom is the "Hog Call" at the University of Arkansas. At kickoffs and other key times during the game, Razorback fans flood the field with a deafening "Woooooooo. Pig. Sooie!" At University of Wisconsin games, tens of thousands of Badger fans shake the stadium with their bouncing as the loud speakers blast House of Pain's "Jump Around." Traditions like these may seem silly, but you can't be self-conscious when the game is on the line!
To train the multimodal encoder, we collect a dataset that consists of video-text pairs from YouTube. We then collect the top searched videos whose duration is 5-20 minutes as well as their corresponding titles and descriptions. Finally, a dataset consisting of 1,069 YouTube Video-Text pairs (YTVT) is collected for the multimodal self-supervised learning. Figure 1 shows an example from the YTVT dataset222More examples can be found in the supplemental materials.. After the self-supervised pretraining, a multimodal sequence encoder is obtained and further fine-tuned for the video summarization task. In this work, we propose a progressive video summarization method using the pretrained multimodal encoder, where the input sequence is refined by iteratively emphasizing the important content in a multi-stage fashion. Besides, we also describe how to incorporate the text information into our framework for video summarization. Practically, we use video classification as the downstream task to evaluate the summaries. The motivation is that 1) a satisfactory summary is supposed to reflect the same class information as the original video; 2) the class labels of videos are way easier to obtain than the frame/shot-wise annotations.


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