{"id":186415,"date":"2024-12-15T12:55:50","date_gmt":"2024-12-15T03:55:50","guid":{"rendered":"http:\/\/ee.presscat.kr\/?post_type=research-achieve&p=186415"},"modified":"2026-04-13T08:47:33","modified_gmt":"2026-04-12T23:47:33","slug":"ee-prof-junmo-kims-team-develop-ai-that-imagines-and-understands-how-images-change-like-humans","status":"publish","type":"research-achieve","link":"http:\/\/ee.presscat.kr\/en\/research-achieve\/ee-prof-junmo-kims-team-develop-ai-that-imagines-and-understands-how-images-change-like-humans\/","title":{"rendered":"EE Prof. Junmo Kim\u2019s Team, Develop AI That Imagines and Understands How Images Change Like Humans"},"content":{"rendered":"
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<Professor Junmo Kim, PhD candidate Jaemyung Yu><\/figcaption><\/figure>\n

A research team led by Professor Junmo Kim from the Department of Electrical Engineering at KAIST has developed an innovative AI technology that can envision and understand how images change, similar to how humans imagine transformations like rotation or recoloring. This breakthrough goes beyond simply analyzing images, enabling the AI to comprehend and express the processes involved in transforming visual data. The technology holds promise for diverse applications, including medical imaging, autonomous driving, and robotics, where precision and adaptability are essential.<\/span><\/p>\n

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AI That Imagine Changes Like Humans (Understands How Images Change, Like Humans)<\/strong><\/span><\/h3>\n

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The newly developed technology, Self-supervised Transformation Learning (STL)<\/strong>, focuses on enabling AI to learn how images transform. STL operates without relying on human-provided labels; instead, it learns transformations by comparing original images with their transformed versions. It independently recognizes changes such as, \u201cThis has been rotated,\u201d or, \u201cThe color has changed.\u201d This process parallels the way humans observe, imagine, and interpret variations in visual data.<\/span><\/p>\n

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\"Illustration
Illustration of the roles of the three representation learning approaches that constitute STL: (a) distinguishing images regardless of transformations, (b) aligning transformation representations for the same transformation applied to different images, and (c) ensuring that relationships between representations of transformed variants of the same image reflect the actual transformation. STL integrates all these roles for comprehensive learning<\/span><\/figcaption><\/figure>\n

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Overcoming the Limitations of Conventional Methods<\/strong><\/span><\/h3>\n

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Traditional AI systems often struggle with subtle transformations, focusing primarily on capturing large, overarching features while ignoring finer details. This limitation becomes a significant challenge in scenarios where precise understanding of intricate changes is crucial.<\/span><\/p>\n

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STL addresses this gap by learning to encode even the smallest transformations in an image into its feature space\u2014a conceptual map representing the relationships between different data points. Rather than ignoring these changes, STL incorporates them into its feature representations, enabling more accurate and nuanced outcomes.<\/span><\/p>\n

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For example, STL excels at recognizing specific alterations, such as random cropping, brightness adjustments, and color modifications, achieving performance improvements of up to 42% over conventional methods. It is particularly adept at handling complex transformations that were previously difficult for AI to manage.<\/span><\/p>\n

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\"Examples
Examples of transformations often ignored by existing methods. These transformations, though subtle, may carry important differences.<\/span><\/figcaption><\/figure>\n

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Smarter AI for Broader Applications<\/strong><\/span><\/h3>\n

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What sets STL apart is its ability to not only understand visual content but also learn and represent transformations themselves. This capability allows STL to detect subtle changes in medical images, such as CT scans, and better interpret diverse conditions in autonomous driving. By incorporating transformations into its understanding, STL can deliver safer and more precise results across various applications.<\/span><\/p>\n

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Toward Human-Like Understanding<\/strong><\/span><\/h3>\n

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\u201cSTL represents a significant leap forward in AI technology, closely mirroring the way humans perceive and interpret changes in images,\u201d said Professor Junmo Kim. \u201cThis approach has the potential to drive innovations in fields such as healthcare, robotics, and self-driving cars, where understanding transformations is critical.\u201d<\/span><\/p>\n

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The research, conducted by Jaemyung Yu, a PhD candidate at KAIST as the first author, was presented at NeurIPS 2024<\/strong>, one of the world\u2019s leading AI conferences, under the title Self-supervised Transformation Learning for Equivariant Representations<\/em>. It was supported by the Ministry of Science and ICT through the Institute of Information and Communications Technology Planning and Evaluation (IITP) as part of the SW StarLab program (No. RS-2024-00439020, Development of Sustainable Real-time Multimodal Interactive Generative AI).<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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