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    88.   39
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Landscape
    ¹ßÇ¥Á¦¸ñ : »ê¾÷ºÐ¾ßº° ÃÊ°Å´ë AIÈ°¿ë µ¿Çâ ¹× »ç·Ê
    ¹ßÇ¥ÀÚ : ¹Ú¿ë¹Î (ÆÀÀå/LG AI¿¬±¸¿ø)
    °­¿¬¿ä¾à : ¡°Enterprise Ready Generative AI"´Â ´ë±Ô¸ð Á¶Á÷ÀÇ ±ÔÁ¤ Áؼö, ¾÷Á¾º° °ËÁõ »ç·Ê, °­·ÂÇÑ º¸¾È Á¶Ä¡, Æ÷°ýÀûÀÎ À§Çè Æò°¡¸¦ ±¸ÇöÇÑ »ý¼ºÇü AI¸¦ ÀǹÌÇÕ´Ï´Ù. ¡°Enterprise Ready Generative AI¡± ´Â ´Ü¼øÇÑ ¿¬±¸ ÇÁ·ÎÁ§Æ®¸¦ ³Ñ¾î ´Ù¾çÇÑ »ê¾÷°ú ƯÁ¤ ±â´É ¿î¿µ Á¶Á÷¿¡ °ÉÃÄ ½ÇÁ¦ ±â´É°ú °¡Ä¡¸¦ ÀÔÁõÇØ¾ß ÇÕ´Ï´Ù. ±×¸®°í, ...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Landscape
    ¹ßÇ¥Á¦¸ñ : ºñ¾ð¾îÀû µ¥ÀÌÅÍ ±â¹Ý ¼ÒÅëÀÇ Çõ½Å
    ¹ßÇ¥ÀÚ : Áø½ÂÇõ (´ëÇ¥/Ŭ·¹¿Â)
    °­¿¬¿ä¾à : Ŭ·¹¿Â(Klleon)Àº ½Ç½Ã°£ ´ëÈ­Çü µðÁöÅÐ ÈÞ¸ÕÀ» °³¹ß ¹× ¼­ºñ½ºÈ­ÇÏ´Â µöÅ×Å© ½ºÅ¸Æ®¾÷ÀÔ´Ï´Ù. ÀÌ ±â¼úÀº ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ È°¿ëµÉ ¼ö ÀÖÀ¸¸ç, ƯÈ÷ ±³À°, ¿£ÅÍÅ×ÀθÕÆ®, ÀÇ·á, °í°´ ¼­ºñ½º µî¿¡¼­ Å« ÀáÀç·ÂÀ» °¡Áö°í ÀÖ½À´Ï´Ù. »ý¼ºÇü AI°¡ ½ÇÁ¦ »ê¾÷¿¡¼­ ¾î¶»°Ô È°¿ëµÇ°í ÀÖ´ÂÁö ±¸Ã¼ÀûÀÎ »ç·ÊµéÀ» °øÀ¯ÇÕ´Ï´Ù.more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Fundamentals
    ¹ßÇ¥Á¦¸ñ : How to Tame Your Large Generative Models?
    ¹ßÇ¥ÀÚ : À¯ÀçÁØ (±³¼ö/UNIST)
    °­¿¬¿ä¾à : We find ourselves in an era dominated by large generative models, which, while demonstrating remarkable performance, demand vast datasets and substantial computational resources. However, these resources remain inaccessible to the majority of researchers and all but a few well-resourced companies. A...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Fundamentals
    ¹ßÇ¥Á¦¸ñ : Generative Reconstruction of 3D Human Models
    ¹ßÇ¥ÀÚ : ÀÓÈ­¼· (´ÜÀå/KIST)
    °­¿¬¿ä¾à : High-quality 3D reconstruction of humans typically requires over 60 synchronized cameras in specialized environments to minimize photometric loss by ensuring the 3D model aligns closely with provided images. The model's quality is adjustable based on capture settings and purpose. Recent generative m...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Fundamentals
    ¹ßÇ¥Á¦¸ñ : Code LLMs and the Transforming Development Environment with AI
    ¹ßÇ¥ÀÚ : ±è¼ºÁÖ (¸®´õ/NAVER Cloud)
    °­¿¬¿ä¾à : Code LLM°ú °°Àº AI ±â¼úÀÌ °³¹ßÀÚ »ý»ê¼ºÀ» ¾î¶»°Ô Çõ½ÅÀûÀ¸·Î Çâ»ó½ÃÅ°´ÂÁö ÁýÁß Á¶¸íÇÕ´Ï´Ù. °³¹ßÀÚ »ý»ê¼º Çâ»ó AI ±â¼úµéÀÌ ¹ßÀüÇÔÀ¸·Î½á °³¹ß ½Ã°£ÀÌ ´ëÆø ÁÙ¾îµé°í ÀÖÀ¸¸ç, ÀÌ´Â °³¹ßÀÚµéÀÌ ¹®Á¦ ÇØ°áÀÇ º»Áú¿¡ ´õ ±íÀÌ ¸ôµÎÇÒ ¼ö ÀÖ°Ô ¸¸µé°í ÀÖ½À´Ï´Ù. ÀÌ ¼¼¼ÇÀºCode LLMÀÇ ¹ßÀü °úÁ¤°ú CodeLLMÀÌ º¯È­½ÃÅ°°í ÀÖ´Â...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Applications/Services
    ¹ßÇ¥Á¦¸ñ : Bespoke Learning & Assessment with Gen AI
    ¹ßÇ¥ÀÚ : ȲÀμ® (±³¼ö/Æ÷Ç×°ø´ë)
    °­¿¬¿ä¾à : As computing services are being planted into one¡¯s everyday life, their life experiences and service experiences increasingly permeate each other. This blending trend calls for a computing service to personalize its embodiment, while keeping its own principles unaltered, such that individual users...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Applications/Services
    ¹ßÇ¥Á¦¸ñ : On-device Generative AI for Video Virtual Try-on
    ¹ßÇ¥ÀÚ : ±èÇü½Å (±³¼ö/¼­¿ï´ë)
    °­¿¬¿ä¾à : We present MIRROR, an on-device video virtual try-on (VTO) system that provides realistic, private, and rapid experiences in mobile clothes shopping. Despite recent advancements in generative adversarial networks (GANs) for VTO, designing MIRROR involves two challenges: (1) data discrepancy due to r...more
    Track > Session : AI-I (Gen AI, Hyperscale AI) > Gen AI Applications/Services
    ¹ßÇ¥Á¦¸ñ : Generating 4D Effects from Audiovisual Stream
    ¹ßÇ¥ÀÚ : Ãֽ¹® (±³¼ö/Æ÷Ç×°ø´ë)
    °­¿¬¿ä¾à : ±Ù·¡ ¿µÈ­¸¦ º¸°Å³ª VR °ÔÀÓÀ» ÇÏ´Â »ç¿ëÀÚ¿¡°Ô ÀϹÝÀûÀÎ ½Ãû°¢ È¿°ú ¿Ü¿¡ ¸ö Àüü¸¦ À̵¿½ÃÅ°°Å³ª ȸÀü½ÃÅ°´Â µ¿ÀÛ È¿°ú, Ãæ°ÝÀ̳ª ¶³¸²À» Á¦°øÇÏ´Â Áøµ¿ È¿°ú, Çâ±â/¹° »Ñ¸®±â µî ´Ù¾çÇÑ 4DÈ¿°ú¸¦ ÇÔ²² Á¦°øÇÏ¿© »ç¿ëÀÚ °æÇèÀÇ »ç½Ç¼º, ¸ôÀÔ¼º, Àç¹Ì µîÀ» Çâ»ó½ÃÅ°°íÀÚ ÇÏ´Â ½Ãµµ°¡ ÀÌ·ç¾îÁö°í ÀÖ´Ù. ½ÇÁ¦·Î 4D ±ØÀåÀº ...more
    Track > Session : Future Mobility > Trustworthy Autonomous Vehicles
    ¹ßÇ¥Á¦¸ñ : ÃÊ°í¼Ó V2X ±â¹Ý ÀÚÀ²Çù·ÂÁÖÇà ±â¼ú ¹× ½ÇÁõ µ¿Çâ
    ¹ßÇ¥ÀÚ : ¼ÛÀ¯½Â (Ã¥ÀÓ/ETRI)
    °­¿¬¿ä¾à : ¼¼°è °¢±¹Àº 2026 ³â ÀÌÈĺÎÅÍ ¿ÏÀüÀÚÀ²ÁÖÇà ¼­ºñ½º »ó¿ëÈ­¸¦ ¸ñÇ¥·Î ¿¬±¸°³¹ßÀ» ÁøÇàÇÏ°í ÀÖ´Ù. º» °­¿¬¿¡¼­´Â ±¹³»¿Ü C-ITS ±â¼ú °³¹ß ÇöȲ°ú ·Îµå¸ÊÀ» ¼Ò°³ÇÑ´Ù. À̾ ¿ÏÀüÀÚÀ²ÁÖÇà¿¡ ÇÊ¿äÇÑ ¾ÈÀü¼º °íµµÈ­¸¦ À§ÇØ Â÷·®¿ë Åë½Å±â¼úÀÇ Çʿ伺, ¿ä±¸µÇ´Â ½Ã½ºÅÛ ±¸Á¶ ¹× °ü·Ã ¾Ë°í¸®Áò µîÀ» ´Ù·é´Ù. ¸¶Áö¸·À¸·Î Â÷·®¿ë ...more
    Track > Session : Future Mobility > Trustworthy Autonomous Vehicles
    ¹ßÇ¥Á¦¸ñ : SDV¸¦ À§ÇÑ Cloud native Â÷·® ¼ÒÇÁÆ®¿þ¾î Ç÷§Æû ±â¼ú
    ¹ßÇ¥ÀÚ : ¼Õµ¿È¯ (´ëÇ¥/¾ËƼ½ºÆ®)
    °­¿¬¿ä¾à : Â÷·® ÀüÀå ¾ÆÅ°ÅØó ¹× ¼ÒÇÁÆ®¿þ¾î °³¹ß °úÁ¤ µî Â÷·® ȯ°æ º¯È­¿¡ µû¸¥ SDVÀÇ Çʿ伺°ú, À̸¦ À§ÇÑ Å¬¶ó¿ìµå ±â¹Ý ¼ÒÇÁÆ®¿þ¾î Ç÷§Æû ±â¼úµé ¹× °ü·Ã Ä¿¹Â´ÏƼ ÇöȲÀ» ¼Ò°³ÇÕ´Ï´Ù.more
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