Polywink - Automatic Expressions Blendshapes and Facial Rigs
Polywink is an online platform created to automate the most time-consuming and expensive aspects of facial animation. Thanks to our in-house procedural technology and through years of accumulated data powered by machine learning, we are able to complete in mere hours steps that would otherwise take weeks to achieve: from blendshapes generation to automatic facial rigging solutions, we provide ever
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Stanford CS 224N | Natural Language Processing with Deep Learning
Logistics Lectures: are on Tuesday/Thursday 4:30 PM - 5:50 PM Pacific Time in NVIDIA Auditorium. The lectures will also be livestreamed on Canvas via Panopto. Lecture videos for enrolled students: are posted on Canvas (requires login) shortly after each lecture ends. Unfortunately, it is not possible to make these videos viewable by non-enrolled students. Publicly available lecture videos and vers
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Innovative Tech： ã“ã®ã‚³ãƒ¼ãƒŠãƒ¼ã§ã¯ã€ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼ã®æœ€æ–°ç ”究を紹介ã™ã‚‹Webメディア「Seamlessã€ã‚’主宰ã™ã‚‹å±±ä¸‹è£•æ¯…æ°ãŒåŸ·ç†ã€‚æ–°è¦æ€§ã®é«˜ã„科å¦è«–文を山下æ°ãŒãƒ”ックアップã—ã€è§£èª¬ã™ã‚‹ã€‚ 米ワシントン大å¦ã®ç ”究ãƒãƒ¼ãƒ ãŒé–‹ç™ºã—ãŸã€ŒPeople as Scene Probesã€ã¯ã€ä¸€æ–¹å‘ã‹ã‚‰æ’®å½±ã—ãŸæ˜ åƒã®å¥¥è¡Œãã‚„å…‰ã®å½“ãŸã‚Šæ–¹ã‚’分æžã™ã‚‹æ·±å±¤å¦ç¿’ベースã®æŠ€è¡“ã 。ã“ã®æŠ€è¡“を使ã†ã“ã¨ã§ã€å½±ã‚„「オクルージョンã€ï¼ˆé®è”½ç‰©ã«ã‚ˆã£ã¦å…‰ã®åå°„ãŒã•ãˆãŽã‚‰ã‚Œã‚‹åº¦åˆã„ã€ç’°å¢ƒé®è”½ã¨ã‚‚） を考慮ã—ãŸãƒªã‚¢ãƒ«ãªç”»åƒåˆæˆãŒå¯èƒ½ã«ãªã‚‹ã€‚ æ˜ åƒå†…ã®ç‰©ä½“ã‹ã‚‰å¥¥è¡Œãã€ã‚ªã‚¯ãƒ«ãƒ¼ã‚¸ãƒ§ãƒ³ã€ç…§æ˜Žï¼å½±ã®æƒ…å ±ã‚’æŠ½å‡ºã—ã€æ–°ã—ã挿入ã™ã‚‹ç”»åƒã«é©å¿œã™ã‚‹ã“ã¨ã§è‡ªç„¶ãªåˆæˆç”»åƒã‚’生æˆã™ã‚‹ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®æ¦‚è¦å›³ã€‚下段ã®å›³ã§ã¯ã€æ‰‹å‰ã®äººç‰©ã‚„奥ã®èµ¤ã„車ã«å¯¾ã—ã¦é©åˆ‡ãªå½±ãŒåŠ ãˆã‚‰ã‚Œã¦ã„ã‚‹
People as Scene Probes
European Conference on Computer Vision (ECCV), 2020 We introduce a fully automatic pipeline for inferring depth, occlusion, and lighting/shadow information from image sequences of a scene. All theses information is extracted just by using people (and other objects such as cars) as scene probes to passively scan the scene. We also develop a tool for image compositing based on the inferred depth, oc
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2. １９４６： ＥＮＩＡＣ，高ã„計算能力 フォン・ノイマン「俺ã®æ¬¡ã«é ã®è‰¯ã„奴ãŒã§ããŸã€ １９５２： Ａ．Ｓï½ï½ï½•ï½…ｌã«ã‚ˆã‚‹ãƒã‚§ãƒƒã‚«ãƒ¼ã‚ºãƒ—ãƒã‚°ãƒ©ãƒ 機械å¦ç¿’ã¨äººå·¥çŸ¥èƒ½ã®æ´å² 2 １９５７：Ｐｅｒｃｅï½ï½”ï½’ï½ï½Žï¼Œãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®å…ˆé§†ã‘ 第一次ニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ–ーム１９６３：線形サãƒãƒ¼ãƒˆãƒ™ã‚¯ãƒˆãƒ«ãƒžã‚·ãƒ³ １９８ï¼å¹´ä»£ï¼šå¤šå±¤ãƒ‘ーセプトãƒãƒ³ï¼Œèª¤å·®é€†ä¼æ¬ï¼Œ 畳ã¿è¾¼ã¿ãƒãƒƒãƒˆ 第二次ニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ–ーム１９９２： éžç·šå½¢ã‚µãƒãƒ¼ãƒˆãƒ™ã‚¯ãƒˆãƒ«ãƒžã‚·ãƒ³ （カーãƒãƒ«æ³•ï¼‰ 統計的å¦ç¿’ 線形モデルã®é™ç•Œ éžå‡¸æ€§ã®å•é¡Œ １９９６： スパースå¦ç¿’ （Lasso） ï¼’ï¼ï¼ï¼“： トピックモデル （LDA） ï¼’ï¼ï¼‘２： Supervision （Alex-net） 第三次ニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ–ームデータã®å¢—åŠ ï¼‹è¨ˆç®—æ©Ÿã®å¼·åŒ– １９６ï¼å¹´ä»£å‰åŠï¼š ＥＬＩＺＡ（イライザ）， 擬似心ç†ç™‚法士 １９８ï¼å¹´ä»£
Deep Tabnine - Tabnine Blog
Update (August 19): We’ve released Tabnine Local, which lets you run Deep Tabnine on your own machine. TL;DR: Tabnine is an autocompleter that helps you write code faster. We’re adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here. There has been a lot of hype about deep learning in the past few years. Neural network
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
https://blog.tensorflow.org/2019/05/introducing-tensorflow-graphics_9.html https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLtbFBddaXSQ9x-oBlhU_mUrk7nd9XpEr0bWXFRiE9lNjdeGKKf6Zl5DKbqn7eiaY032Wmck2JDliqalyJx_f43Y-4Mspjkz3X7eBze1o7NhSnsyDRvEIpHli9oqEFBuJshVeRt7cEmW02/s1600/tf+graphics.jpeg May 09, 2019 — Posted by Julien Valentin and Sofien Bouaziz Github repository: https://github.com/te
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ディープラーニング入門：Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«
Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ« æ•°å¦ã®åŸºç¤Žã€ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªž Python ã®åŸºç¤Žã‹ã‚‰ã€æ©Ÿæ¢°å¦ç¿’・ディープラーニングã®ç†è«–ã®åŸºç¤Žã¨ã‚³ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã¾ã§ã‚’幅広ã解説 ※Chainerã®é–‹ç™ºã¯ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ãƒ¢ãƒ¼ãƒ‰ã«å…¥ã‚Šã¾ã—ãŸã€‚詳ã—ãã¯ã“ã¡ã‚‰ã‚’ã”覧ãã ã•ã„。 何ã‹ã‚‰å¦ã¶ã¹ãã‹è¿·ã‚ãªã„ ディープラーニングをå¦ã¶ã«ã¯ã€å¤§å¦ã§å¦ã¶ãƒ¬ãƒ™ãƒ«ã®æ•°å¦ã‚„ Python ã«ã‚ˆã‚‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®çŸ¥è˜ã«åŠ ãˆã¦ã€ Chainer ã®ã‚ˆã†ãªãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークã®ä½¿ã„æ–¹ã¾ã§ã€å¹…広ã„知è˜ãŒå¿…è¦ã¨ãªã‚Šã¾ã™ã€‚ 本ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€åˆå¦è€…ã«ã‚ˆãã‚る「ã¾ãšä½•ã‚’å¦ã¹ã°è‰¯ã„ã‹ã€ãŒåˆ†ã‹ã‚‰ãªã„〠ã¨ã„ã†å•é¡Œã‚’解決ã™ã‚‹ãŸã‚ã«è¨è¨ˆã•ã‚Œã¾ã—ãŸã€‚ åˆå¦è€…ã¯ã€Œã¾ãšä½•ã‚’ã€ãã—ã¦ã€Œæ¬¡ã«ä½•ã‚’ã€ã¨è¿·ã†ã“ã¨ãªãã€å¿…è¦ãªçŸ¥è˜ã‚’é †ç•ªã«å¦ç¿’ã§ãã¾ã™ã€‚ å‰æ知è˜ã‹ã‚‰è§£èª¬ ã“ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€Chainer ãªã©ã®ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークを使ã£ãŸãƒ—ãƒã‚°
Limitations of Deep Learning for Vision, and How We Might Fix Them
Limitations of Deep Learning for Vision, and How We Might Fix Them History of Deep Learning We are witnessing the third rise of deep learning. The first two waves — 1950s–1960s and 1980s–1990s — generated considerable excitement but slowly ran out of steam, since these neural networks neither achieved their promised performance gains nor aided our understanding of biological vision systems. The th
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