{"id":20927,"date":"2024-12-06T08:57:34","date_gmt":"2024-12-06T13:57:34","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=20927"},"modified":"2024-12-12T10:03:20","modified_gmt":"2024-12-12T15:03:20","slug":"vision-envisioned","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2024\/12\/vision-envisioned\/","title":{"rendered":"Vision Envisioned"},"content":{"rendered":"\"image\n

The human brain is an absolute marvel of computing power. Estimates suggest the 3 pounds of tofulike tissue in our skulls can perform roughly 1 quintillion calculations per second\u2014a feat only recently matched by the world\u2019s top supercomputers.<\/strong><\/p>\n

Yet that statistic, albeit impressive, fails to convey just how far the brain\u2019s remarkable capabilities go beyond those of machines. Consider our sense of vision. About half the neurons in our cerebral cortexes\u2014the big, lobed, wrinkly outer layer of our brains\u2014play a role in our visual system. That system near instantly handles visual tasks we humans find utterly mundane but that can flummox artificial intelligences. For example, a common cybersecurity tactic involves identifying which images in a set have, say, a bus or a bicycle in them\u2014an easy \u201cchallenge\u201d for humans that stumps online bots.<\/p>\n

Although computer vision is not up to human snuff just yet, scientists have nevertheless made astonishing advancements since its inception in the 1960s. Nowadays, facial recognition apps on new smartphones have become de rigueur. Self-driving cars are on the cusp of wider adoption, having logged tens of millions of miles on public roads and compiling a safety record that some say surpasses that of human drivers. The medical profession is likewise getting closer to broadly adopting computer vision for clinic use to assist with detecting tumors and other abnormalities.<\/p>\n

Over the last four decades, Johns Hopkins\u2019 Alan Yuille<\/a><\/strong> has made a significant impact on the overall development of computer\"A vision and is also continuing to advance the field toward more humanlike abilities. Yuille has drawn inspiration from the human visual system\u2014refined in our species over millions of years of evolution \u2014for key insights into advancing computer vision capabilities.<\/p>\n

\u201cI want algorithms that will work in the real world and that will perform at the level of humans, probably ultimately better. And to do that, I think we need to get inspired by the brain,\u201d says Yuille, Bloomberg Distinguished Professor of computer science and cognitive science at the Whiting School of Engineering and Krieger School of Arts and Sciences.<\/p>\n

Yuille\u2019s career has spanned multiple institutions and eras in computer vision, from the early conceptual days through the revolution of machine learning, where AI algorithms devour massive datasets of imagery and learn like humans. His contributions are tightly interwoven into the fabric of the field. Examples include semantic segmentation, where computers distinguish classes of objects and backgrounds at the level of individual pixels, to compositionality, where the whole of an object can be represented by the aggregate of its parts.<\/p>\n

\u201cWhat is so fascinating is that when you go to conferences, even the new young superstars, they know about Alan and are inspired by him,\u201d says Adam Kortylewski, a former postdoctoral researcher in Yuille\u2019s lab at Johns Hopkins and currently a group leader at the University of Freiburg and the Max Planck Institute for Informatics.<\/p>\n

Kortylewski credits Yuille\u2019s career success to his enduring perspectives on vision and openness to innovation.<\/p>\n

\u201cAlan is convinced of certain ideas, but he\u2019s also flexible enough to then adapt to new insights and new technology. I think he\u2019s one of the very few figures who has this kind of capability,\u201d Kortylewski says. \u201cSo even today, the research and new papers coming from his lab are very well-cited and are pioneering work that other people catch up with.\u201d<\/p>\n\"black-and-white\n

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\u2019ADVENTUROUS\u2019 AI <\/span><\/h4>\n

Yuille\u2019s path to collectively studying natural cognition, artificial intelligence, and computer vision was far from linear. He grew up in North London in an area known as Highgate, bordering a cemetery. (\u201cKarl Marx was buried over behind our backyard wall,\u201d Yuille relates.) His parents, from Australia originally, were into the liberal arts\u2014his mother in English literature, his father in architecture. Yuille remembers there being thousands of books about visual art in his home. Along with frequent trips to art museums, he thinks the exposure must have made an impression, despite his lackluster interest. \u201cI think at the time, I rather reacted against it,\u201d Yuille jokes.<\/p>\n

What did rivet Yuille, though, was sports, and as an adult, Yuille continues to find active outlets. \u201cI hike up mountains, I ski down mountains. I\u2019ve done hang gliding. I\u2019ve flown a small airplane,\u201d he says. He chalks up his adventure-seeking to \u201ctoo many Bond films when I was a boy.\u201d<\/p>\n

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Stephen Hawking, (foreground right) meets with a group of his doctoral students at Cambridge University during the late 1970s. Alan Yuille is shown seated two seats to Hawking’s right. [Photo courtesy of Alan Yuille]<\/figcaption><\/figure>\n

In college, Yuille first became fascinated by mathematics and Albert Einstein\u2019s general theory of relativity, the prevailing, century-old explanation for gravity. That pursuit led to a PhD in theoretical physics from the University of Cambridge in 1981, supervised by one of the most famous physicists of all time, the late Stephen Hawking, whom Yuille fondly recalls.<\/p>\n

\u201cStephen didn\u2019t take things too seriously,\u201d he says, recounting one afternoon when Hawking, having already developed severe symptoms from amyotrophic lateral sclerosis, had Yuille handle the computer interface for a Dungeons and Dragons game Hawking wanted to play.<\/p>\n

However, Yuille\u2019s research\u2014involving the fiendishly still-unresolved quandary of quantum gravity\u2014foundered, leaving him looking for something new but similarly profound. \u201cI thought, \u2019Well, AI was sort of something as fundamental as understanding the basic physical laws of the universe.\u2019 Because AI is really understanding human intelligence, and that\u2019s equally fundamental,\u201d says Yuille.<\/p>\n

Compared to physics, the field of AI was also attractively far less developed, plus more practical.<\/p>\n

\u201cI liked the fact that AI would have real-world consequences,\u201d says Yuille. \u201cSo that got me into leaving physics and doing AI\u2014because it was new, it was somewhat adventurous.\u201d<\/p><\/blockquote>\n

VISUAL<\/span> RUDIM<\/span>ENTS<\/span>\"black-and-white\"color<\/span><\/strong><\/h4>\n

Pursuing this newly embraced academic space, Yuille joined the Artificial Intelligence Laboratory at MIT in the early 1980s. Perhaps hearkening back to his childhood exposure to the visual arts, he focused his research on computer vision\u2014a fledgling field begun at MIT barely 15 years prior.<\/p>\n

\u201cIn the 1980s, it was almost like the Wild West,\u201d says Yuille. \u201cWe had to start making sense of the whole enormous complexity of vision.\u201d<\/p>\n

Phenomenologically, vision involves particles of light, called photons, being emitted or reflected by matter and then striking the retinal cells at the back of our eyeballs. Generated nerve impulses subsequently travel to our brain\u2019s visual cortex. There, vast networks of neurons process the streamed-in information and construct our richly perceived world of color, shape, textures, and distances.<\/p>\n

Seeking a way into this morass, Yuille teamed up with Tomaso Poggio, who had also just joined the faculty at MIT. \u201cWe hit it off almost immediately and started working together, maybe because both of us were physicists originally and not computer scientists,\u201d recalls Poggio, who stayed on at MIT and is now the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences.<\/p>\n

In focusing on intrinsic elements of human visual perception and cognition, Yuille and \u201cTommy,\u201d as he affectionately calls Poggio, studied how computers might perceive the basic lines and edges of objects to make broad inferences about the contents of a visual field. \u201cFinding reliable contours of objects and edges in images at the time was one of the early problems in computer vision,\u201d says Poggio.<\/p>\n

During this early phase, much of the work was conceptual and could not be deeply demonstrated, owing to the lack of computing power and massive datasets that would later transform the field. Yet being restrained in this way meant that Yuille and colleagues had to proceed from a more theoretical basis, getting at the conceptual underpinnings of vision rather than worrying about go-to-market applications. \u201cI think a lot of the ideas we had were actually pretty good,\u201d says Yuille, \u201cbut we didn\u2019t have the models that could take advantage of the big data that we now have.\u201d<\/p>\n\"black-and-white\n

MENTAL<\/span> MO<\/span>DELS<\/span> FOR VISION<\/span>\"color<\/span><\/strong><\/h4>\n

Building on his research progress in computer vision, Yuille moved to Harvard University in the mid \u201980s and became a professor. From there, in 1995, he went to the Smith-Kettlewell Eye Research Institute, a nonprofit research organization in San Francisco. Seven years later, he returned to academia at UCLA as a professor with joint appointments in statistics, computer science, psychiatry, and psychology, an indication of how his research had become highly multidisciplinary.<\/p>\n

Some of the areas where Yuille broke ground along the way include the aforementioned semantic segmentation, which is a core task for self-driving cars as they find where the road is, where boundaries are, and discern other cars. Another related area where Yuille introduced key ideas\u2014also tying into the combining-parts-to-make-the-whole concepts of compositionality\u2014is termed \u201canalysis by synthesis.\u201d<\/p>\n

The essential notion of analysis by synthesis is that, through experience of the world, humans build up a vast mental library of possible objects. During moment-to-moment vision, we constantly and rapidly form hypotheses about the identity of the objects we are likely seeing. Based on that mental library, we then cognitively synthesize the hypothesized object, forming a manipulable, virtual version of it. As the milliseconds pass, we continue comparing the observed object to the synthesized object, filling in details and ultimately determining what the object is.<\/p>\n

\u201cIt\u2019s like you\u2019re saying, \u2019OK, we\u2019re pretty sure this is a face at this angle, and now let\u2019s check and get the details correct exactly about whose face it is, where the eyes are if they\u2019re looking at you,\u2019 things like that,\u201d says Yuille.<\/p>\n

APPREHENDING<\/span> CA<\/span>NCER<\/span><\/h4>\n

On account of Yuille\u2019s reputation, based on these and other breakthroughs in computer vision, he was recruited by Johns Hopkins in 2016 to be a Bloomberg Distinguished Professor. A $350 million endowment from Michael R. Bloomberg \u201964<\/strong> established these influential positions at the university in 2013 to foster innovatively interdisciplinary collaborations aimed at tackling particularly complex problems.<\/p>\n

\u201cI was attracted to Hopkins because of the idea of trying to relate the study of biology or cognitive science to the study of AI,\u201d says Yuille.<\/p><\/blockquote>\n

A fruit of this labor has been Yuille\u2019s trailblazing research into using computer vision to detect tumors in CT scans. A project dedicated to this approach for pancreatic cancer, one of the deadliest malignancies, is known as FELIX, named after a magical potion, Felix Felicis or \u201cLiquid Luck,\u201d from the Harry Potter<\/em> series.<\/p>\n

The FELIX project has brought together Yuille\u2019s expertise in computer vision and deep neural networks\u2014\u201cdeep nets,\u201d in the jargon\u2014with that of Elliot Fishman, a professor of radiology and oncology at the Johns Hopkins University School of Medicine. Clinicians have sought to boost rates of detecting pancreatic cancer early, when a cure is still possible. Human radiologists fail to detect tumors in about 40% of CT scans of patients whose tumors are still small (less than 2 centimeters) and surgically removable. The hope is that machine learning algorithms can act as a second pair of expert eyes to review scans and alert radiologists to tumors that they might have otherwise missed.<\/p>\n

\u201cThe goal is to bring this technology to the clinic so one day, whenever anyone gets a CT scan, they will have the help of this AI friend,\u201d says Bert Vogelstein, MED \u201974 (MD), the Clayton Professor of Oncology at the School of Medicine and a participant in the FELIX project.<\/p>\n

Yuille recognized that semantic segmentation and other computer vision techniques could accelerate the project, along with efforts to simulate rare, early tumors for more effective training of the algorithms. \u201cAlan has tremendous insights into the underlying principles of deep networks and other machine learning algorithms,\u201d says Vogelstein.<\/p>\n

The FELIX project overall has achieved excellent performance, closely matching human skill at detecting larger tumors, while also revealing dangerously tiny ones. In total, about one-quarter of the pancreatic cancers FELIX has picked up had not been previously diagnosed, illustrating the approach\u2019s immense promise.<\/p>\n

Ongoing work continues to validate FELIX for innovative clinical use and extend its value to detecting cancer in other abdominal organs. \u201cThese forms of automatic diagnosis could obviously be huge,\u201d says Yuille.<\/p>\n\"black-and-white\n

THE FUTURE OF COMPUTER VISION <\/span><\/h4>\n

As he has expanded the frontier of what\u2019s possible in computer vision over decades, Yuille has also educated hundreds of students, many of whom have gone on to build upon their former teacher\u2019s results.<\/p>\n

\u201cAlan is a great mentor,\u201d says computer vision researcher Cihang Xie, who first took a machine learning course of Yuille\u2019s during his master\u2019s degree studies at UCLA, then later joined Yuille\u2019s lab at Johns Hopkins to obtain his PhD in 2020. He is now an assistant professor of computer science and engineering at the University of California, Santa Cruz.<\/p>\n

\u201cAlan is quite open to new directions, and he respects your ideas,\u201d Xie adds. \u201cHe is always very patient in completely understanding what you\u2019re trying to do and help you in building a more mature research case, even when it\u2019s something he also needs to learn.\u201d<\/p>\n

Xie worked on compositional models with Yuille, among other topics, and has seen how combining them with deep nets is growing the technology. \u201cAlan started these deep compositional models that are more powerful and pretty robust to [object] occlusion or other harder scenarios that cannot be solved by regular deep learning models,\u201d says Xie.<\/p>\n

The advancements keep on coming. In work presented at an international conference in July 2024, for instance, Xie, Yuille, and their students demonstrated an enhanced version of image-GPT, a visual equivalent to the celebrated chatbot ChatGPT. Just as these large language models can predict the next words in a sentence to form (usually) coherent statements, image-GPT predicts next pixels, generating images\u2014for instance, of people, animals, cars, buildings, and so on. By incorporating more contextually semantic predictive approaches, the researchers achieved 90% classification accuracy on a benchmark dataset\u2014the best performance shown in academia and on par with big tech companies such as Google and Meta. \u201cWe constructed a state-of-the-art image recognition model, and we\u2019re pretty excited about it,\u201d says Xie.<\/p>\n

\u2019THE HUMAN<\/span> ELE<\/span>MENT\u2019<\/span><\/h4>\n

Amid successes in the dramatic rise of computer vision, Yuille emphasizes that for the field to evolve further, it must embrace the human visuocognitive experience of lived-in environments. \u201cHuman vision is developed by interacting with the world as infants; I see it with my son,\u201d Yuille explains. Rather than just looking at tons of images, \u201cyou explore, you touch objects with your hand, you build up a knowledge model.\u201d \u201cI think if we want to take computer vision algorithms up to the next level,\u201d Yuille concludes, \u201cwe\u2019ll need to get that human element back in.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

Bestowing machines with the ability to perceive the physical world as humans do has been a careerlong mission of Alan Yuille, a pioneer in the field of computer vision.<\/p>\n","protected":false},"author":30,"featured_media":21287,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"class_list":["post-20927","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-features","issue-winter-2025"],"acf":[],"yoast_head":"\nVision Envisioned - JHU Engineering Magazine<\/title>\n<meta name=\"description\" content=\"Over the last four decades, Johns Hopkins\u2019 Alan Yuille has made a significant impact on the overall development of computer vision and is also continuing to advance the field toward more humanlike abilities.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2024\/12\/vision-envisioned\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Vision Envisioned - 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