{"id":5801,"date":"2020-12-22T10:49:46","date_gmt":"2020-12-22T02:49:46","guid":{"rendered":"https:\/\/pairlabs.ai\/?p=5801"},"modified":"2021-01-07T16:58:48","modified_gmt":"2021-01-07T08:58:48","slug":"acm-icmr-2021-grand-challenge-pair-competition","status":"publish","type":"post","link":"https:\/\/pairlabs.ai\/en\/acm-icmr-2021-grand-challenge-pair-competition\/","title":{"rendered":"ACM ICMR 2021 Grand Challenge: PAIR Competition"},"content":{"rendered":"<h3 style=\"text-align: left;\"><span style=\"font-size: 12pt;\">Challenge Title: <\/span><\/h3>\n<p style=\"text-align: center;\"><strong><span style=\"font-size: 12pt;\">Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries<\/span><\/strong><\/p>\n<h3 style=\"text-align: left;\"><span style=\"font-size: 12pt;\">Registration URL:<\/span><\/h3>\n<p style=\"text-align: center;\"><a href=\"https:\/\/aidea-web.tw\/icmr\"><strong>https:\/\/aidea-web.tw\/icmr<\/strong><\/a><\/p>\n<h3><span style=\"font-size: 12pt;\">Competition Start Date:\u00a0<\/span><\/h3>\n<p style=\"text-align: center;\"><span style=\"font-size: 12pt;\"><strong>01\/04\/2021<\/strong><\/span><\/p>\n<h3><span style=\"font-size: 12pt;\">Challenge Description:<\/span><\/h3>\n<p>Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.<\/p>\n<p>In this competition, we encourage the participants to design object detection models that can be applied in Taiwan\u2019s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Regular Awards<\/strong><\/p>\n<p>According to the points of each team in the final evaluation, we select the highest three teams for regular awards.<\/p>\n<ol>\n<li>Champion: $USD 1,500<\/li>\n<li>1<sup>st<\/sup> Runner-up: $USD 1,000<\/li>\n<li>3<sup>rd<\/sup>-place $USD 750<\/li>\n<\/ol>\n<p><strong>Special Awards <\/strong><\/p>\n<ol>\n<li>Best accuracy award \u2013 award for the highest mAP in the final competition: $USD 200;<\/li>\n<li>Best bicycle detection award \u2013 award for the highest AP of bicycle recognition in the final competition: $USD 200;<\/li>\n<li>Best scooter detection award \u2013 award for the highest AP of scooter recognition in the final competition: $USD 200;<\/li>\n<\/ol>\n<p>All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.<\/p>\n<p>&nbsp;<\/p>\n<h3>Host Organization:<\/h3>\n<p><strong>Pervasive Artificial Intelligence Research (PAIR) Labs, <\/strong><strong>National Chiao Tung University (NCTU), Taiwan<\/strong><\/p>\n<p>The Pervasive AI Research (PAIR) Labs, a group of national research labs funded by the Ministry of\u00a0Science and Technology, Taiwan, is commissioned to achieve academic excellence, nurture\u00a0local AI talents, build international linkage, and develop pragmatic approaches in the areas of\u00a0applied AI technologies toward services, products, workflows, and supply chains innovation\u00a0and optimization. PAIR is constituted of 18 distinguished research institutes in Taiwan to conduct\u00a0research in various of applied AI areas.\u00a0Website:\u00a0<a href=\"https:\/\/pairlabs.ai\/\"><u>https:\/\/pairlabs.ai\/<\/u><\/a><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-5819 aligncenter\" src=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo.png\" alt=\"\" width=\"199\" height=\"153\" srcset=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo.png 892w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo-260x200.png 260w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo-768x591.png 768w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo-705x542.png 705w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/pairlogo-450x346.png 450w\" sizes=\"auto, (max-width: 199px) 100vw, 199px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h3>Industry Partner:<\/h3>\n<p><strong>MediaTek<\/strong><\/p>\n<p>MediaTek Inc. is a Taiwanese fabless semiconductor company that provides chips for wireless communications, high-definition television, handheld mobile devices like smartphones and tablet computers, navigation systems, consumer multimedia products and digital subscriber line services as well as optical disc drives. MediaTek is known for advances in multimedia, AI and expertise delivering the most power possible \u2013 when and where needed. MediaTek\u2019s chipsets are optimized to run cool and super power-efficient to extend battery life. Always a perfect balance of high performance, power-efficiency, and connectivity.\u00a0Web Site: <a href=\"https:\/\/www.mediatek.com\/\">https:\/\/www.mediatek.com\/<\/a><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-5823\" src=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/mtklogo.png\" alt=\"\" width=\"234\" height=\"67\" srcset=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/mtklogo.png 507w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/mtklogo-300x86.png 300w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/mtklogo-450x129.png 450w\" sizes=\"auto, (max-width: 234px) 100vw, 234px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>Academic Partner:<\/h3>\n<p><strong>Intelligent Vision System\u00a0 (IVS) Lab, National Chiao Tung University (NCTU), Taiwan <\/strong><\/p>\n<p>The Intelligent Vision System (IVS) Lab at National Chiao Tung University is directed by Professor Jiun-In Guo. We are tackling practical open problems in autonomous driving research, which focuses on intelligent vision processing systems, applications, and SoC exploiting deep learning technology.\u00a0Web Site: <a href=\"http:\/\/ivs.ee.nctu.edu.tw\/ivs\/\">http:\/\/ivs.ee.nctu.edu.tw\/ivs\/<\/a><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-5821\" src=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/VSlablogo.png\" alt=\"\" width=\"304\" height=\"80\" srcset=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/VSlablogo.png 479w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/VSlablogo-300x79.png 300w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/VSlablogo-450x118.png 450w\" sizes=\"auto, (max-width: 304px) 100vw, 304px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Wistron-NCTU Embedded Artificial Intelligence Research Center<\/strong><\/p>\n<p>Sponsored by Wistron and founded in 2020 September, Wistron-NCTU Embedded Artificial Intelligence Research Center (E-AI RDC) is a young and enthusiastic research center leaded by Prof. Jiun-In Guo (Institute of Electronics, National Chiao Tung University) aiming at developing the key technology related to embedded AI applications, ranging from AI data acquisition and labeling, AI model development and optimization and AI computing platform development with the help of easy to use AI toolchain (called ezAIT). The target applications cover AIoT, ADAS\/ADS, smart transportation, smart manufacturing, smart medical imaging, and emerging communication systems. In addition to developing the above-mentioned technology, E-AI RDC will also collaborate with international partners as well as industrial partners in cultivating the talents in the embedded AI field to further enhance the industrial competitiveness in Taiwan Industry.<\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-5833\" src=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo.png\" alt=\"\" width=\"259\" height=\"141\" srcset=\"https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo.png 834w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo-300x164.png 300w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo-768x419.png 768w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo-705x385.png 705w, https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/12\/wistron-nctulogo-450x246.png 450w\" sizes=\"auto, (max-width: 259px) 100vw, 259px\" \/><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Challenge Title: Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries Registration URL: https:\/\/aidea-web.tw\/icmr Competition Start Date:\u00a0 01\/04\/2021 Challenge Description: Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":5816,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[126],"tags":[147,168],"class_list":["post-5801","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category--en","tag-deep_learning-en","tag-image_identification-en"],"_links":{"self":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/5801","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/comments?post=5801"}],"version-history":[{"count":5,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/5801\/revisions"}],"predecessor-version":[{"id":5869,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/5801\/revisions\/5869"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/media\/5816"}],"wp:attachment":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/media?parent=5801"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/categories?post=5801"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/tags?post=5801"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}