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class="site-content-contain"> <div id="content" class="site-content"> <div id="primary" class="content-area"> <main id="main" class="site-main"> <article id="post-21" class="twentyseventeen-panel post-21 page type-page status-publish hentry" > <div class="panel-content"> <div class="wrap"> <header class="entry-header"> <h2 class="entry-title">Home</h2> </header> <div class="entry-content"> <h2>Lab Introduction</h2> <p>IRIS computer vision lab is a unit of USC鈥檚 School of Engineering. It was founded in 1986 and has been a major center of government- and industry-sponsored research in computer vision and machine learning. The lab has been active in a number of research topics including object detection and recognition, face identification, 3-D modeling from a sequence of images, activity recognition, video retrieval and integration of vision with natural language queries. More details can be found聽<a href="https://sites.usc.edu/iris-cvlab/research/">here</a>. Multiple PhD positions are available, details can be found聽<a href="https://sites.usc.edu/iris-cvlab/position/">here</a>.</p> <h2 class="feature-title">Research Directions</h2> <ul> <li><a href="https://sites.usc.edu/iris-cvlab/research/">Phase Grounding</a></li> <li><a href="https://sites.usc.edu/iris-cvlab/research/">Video Temporal Analysis</a></li> <li><a href="https://sites.usc.edu/iris-cvlab/research/">Face Recognition</a></li> <li><a href="https://sites.usc.edu/iris-cvlab/research/">Unsupervised 3D Geometry Learning</a></li> </ul> <h2 class="feature-title">Resources</h2> <ul> <li><a href="http://conferences.visionbib.com/Iris-Conferences.html" target="_blank" rel="noopener noreferrer">Calendar of Computer Image Analysis, Computer Vision Conferences</a></li> <li><a href="http://iris.usc.edu/Vision-Notes/bibliography/contents.html" target="_blank" rel="noopener noreferrer">Annotated Computer Vision Bibliography</a></li> </ul> <p> </p> </div> </div> </div> </article> <article id="panel1" class="twentyseventeen-panel post-2083 page type-page status-publish hentry" > <div class="panel-content"> <div class="wrap"> <header class="entry-header"> <h2 class="entry-title">Research</h2> </header> <div class="entry-content"> <h2>Object Grounding<a href="https://sites.usc.edu/iris-cvlab/files/2021/05/WechatIMG36.png"><img loading="lazy" decoding="async" class="wp-image-2285 size-full aligncenter" src="https://sites.usc.edu/iris-cvlab/files/2021/05/WechatIMG36.png" alt="" width="496" height="347" srcset="https://sites.usc.edu/iris-cvlab/files/2021/05/WechatIMG36.png 496w, https://sites.usc.edu/iris-cvlab/files/2021/05/WechatIMG36-300x210.png 300w" sizes="(max-width: 496px) 100vw, 496px" /></a></h2> <p>Object grounding refers to localize entities referred by the given natural language phrases. This requires associating words and phrases from language modality to objects and relations in the visual modality. This task is a challenging problem due to significant variations in objects and descriptive queries, which requires the model to understand both language and image as well as the relationship between them. We made progress in object grounding and achieved the state-of-the-art performances for both image and video object grounding on different settings with models named LSEP (<a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Sadhu_Video_Object_Grounding_Using_Semantic_Roles_in_Language_Description_CVPR_2020_paper.pdf">WACV2021</a>), ZSG-Net (<a href="https://openaccess.thecvf.com/content_ICCV_2019/papers/Sadhu_Zero-Shot_Grounding_of_Objects_From_Natural_Language_Queries_ICCV_2019_paper.pdf">ICCV2019</a>) and VOG-Net (<a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Sadhu_Video_Object_Grounding_Using_Semantic_Roles_in_Language_Description_CVPR_2020_paper.pdf">CVPR 2020</a>).</p> <h2>Learning with Less Labels</h2> <p><img loading="lazy" decoding="async" class="alignleft size-full wp-image-2287" src="https://sites.usc.edu/iris-cvlab/files/2021/05/Screen-Shot-2021-05-03-at-10.58.08-AM.png" alt="" width="983" height="467" srcset="https://sites.usc.edu/iris-cvlab/files/2021/05/Screen-Shot-2021-05-03-at-10.58.08-AM.png 983w, https://sites.usc.edu/iris-cvlab/files/2021/05/Screen-Shot-2021-05-03-at-10.58.08-AM-300x143.png 300w, https://sites.usc.edu/iris-cvlab/files/2021/05/Screen-Shot-2021-05-03-at-10.58.08-AM-768x365.png 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /></p> <p>Deep neuron networks typically requires large amount of data for training to achieve good performance. However, it is challenging to obtain fully annotated datasets for every task. Meanwhile, human typically does not require hundreds of guided examples to learn new concepts. With the structured knowledge and recognition systems, we typically only need very limited amount of new knowledge to learn a concept. Many techniques have been developed to address this problem, include but not limit to few-shot learning, semi-supervised learning, self-supervised learning, unsupervised learning, continuous learning and domain adaptation. We have developed SimPLE (<a href="https://arxiv.org/pdf/2103.16725.pdf">CVPR2021</a>) that achieves the state-of-art performance in semi-supervised learning.</p> <p> </p> <h2>Media Forensics Detection</h2> <p><a href="https://sites.usc.edu/iris-cvlab/files/2019/04/phase_grounding.png"><img loading="lazy" decoding="async" class="wp-image-2139 size-full aligncenter" src="https://sites.usc.edu/iris-cvlab/files/2019/04/phase_grounding.png" alt="" width="472" height="400" srcset="https://sites.usc.edu/iris-cvlab/files/2019/04/phase_grounding.png 472w, https://sites.usc.edu/iris-cvlab/files/2019/04/phase_grounding-300x254.png 300w" sizes="(max-width: 472px) 100vw, 472px" /></a></p> <p class="col-md-8">In this project, we focus on finding the semantic inconsistency between visual and language modalities. Specifically, we are actively working on the phrase grounding problem: Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. Compared to previous methods, we leverage context information as well as external knowledge to further boost phrase grounding system鈥檚 performance. In supervised and unsupervised scenarios, we have both achieved the state-of-the-art performance, which are named as MSRC (<a href="http://wind09.github.io/docs/p23-chen.pdf" target="_blank" rel="noopener noreferrer">ICMR17</a>), QRC-Net (<a href="https://arxiv.org/pdf/1708.01676.pdf" target="_blank" rel="noopener noreferrer">ICCV17</a>), KAC-Net (<a href="https://arxiv.org/pdf/1803.03879.pdf" target="_blank" rel="noopener noreferrer">CVPR18</a>) and SPAN (<a href="https://arxiv.org/pdf/2009.00726.pdf">ECCV2020</a>) respectively.</p> <h2>Video Temporal Analysis</h2> <p><a href="https://sites.usc.edu/iris-cvlab/files/2019/04/video-1.png"><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2191" src="https://sites.usc.edu/iris-cvlab/files/2019/04/video-1.png" alt="" width="954" height="450" srcset="https://sites.usc.edu/iris-cvlab/files/2019/04/video-1.png 954w, https://sites.usc.edu/iris-cvlab/files/2019/04/video-1-300x142.png 300w, https://sites.usc.edu/iris-cvlab/files/2019/04/video-1-768x362.png 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /></a><br /> <a href="https://sites.usc.edu/iris-cvlab/files/2019/04/video_2.png"><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2144" src="https://sites.usc.edu/iris-cvlab/files/2019/04/video_2.png" alt="" width="918" height="512" srcset="https://sites.usc.edu/iris-cvlab/files/2019/04/video_2.png 918w, https://sites.usc.edu/iris-cvlab/files/2019/04/video_2-300x167.png 300w, https://sites.usc.edu/iris-cvlab/files/2019/04/video_2-768x428.png 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /></a></p> <p class="col-md-8">Capturing and sharing videos has become much easier since the development of mobile phones and internet, and the demand for semantic analysis of user generated videos is fast growing. Videos, different from images, introduce a unique dimension: time. Thus, in the video domain, temporal analysis is an important research directions and has many exciting topics, such as temporal activity detection(<a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Gao_TURN_TAP_Temporal_ICCV_2017_paper.pdf" target="_blank" rel="noopener noreferrer">TURN, ICCV2017</a>;聽<a href="https://arxiv.org/pdf/1705.01180.pdf" target="_blank" rel="noopener noreferrer">CBR, BMVC2017</a>), language-based video search (<a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Gao_TALL_Temporal_Activity_ICCV_2017_paper.pdf" target="_blank" rel="noopener noreferrer">TALL, ICCV2017</a>), action anticipation(<a href="https://arxiv.org/pdf/1707.04818.pdf" target="_blank" rel="noopener noreferrer">RED, BMVC2017</a>), video-based question answering(<a href="https://arxiv.org/pdf/1803.10906.pdf" target="_blank" rel="noopener noreferrer">Co-Memory, CVPR2018</a>) and so on. Temporal perception and reasoning in large-scale videos is a difficult problem, one has to tackle many challenges, such as complex temporal structures in videos, large video variation and large problem scale.</p> <p> </p> <h2>Face Recognition</h2> <p><a href="https://sites.usc.edu/iris-cvlab/files/2019/04/face_recognition.png"><img loading="lazy" decoding="async" class="size-full wp-image-2137 aligncenter" src="https://sites.usc.edu/iris-cvlab/files/2019/04/face_recognition.png" alt="" width="955" height="638" srcset="https://sites.usc.edu/iris-cvlab/files/2019/04/face_recognition.png 955w, https://sites.usc.edu/iris-cvlab/files/2019/04/face_recognition-300x200.png 300w, https://sites.usc.edu/iris-cvlab/files/2019/04/face_recognition-768x513.png 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /></a></p> <p class="col-md-8">Unconstrained face recognition in the wild is a fundamental problem in computer vision. It aims at matching any face in static images or videos with faces of interest (gallery set). This task is a challenging problem due to large variations in face scales, poses, illumination and blurry faces in videos. A systematic pipeline is required, involving different tasks. We have made progresses in face detection and landmark localization (<a href="https://arxiv.org/abs/1609.03536" target="_blank" rel="noopener noreferrer">CVPR2017</a>,聽<a href="http://www.bmva.org/bmvc/2016/papers/paper095/paper095.pdf" target="_blank" rel="noopener noreferrer">BMVC2017</a>), 3D face modeling(<a href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Tran_Regressing_Robust_and_CVPR_2017_paper.pdf" target="_blank" rel="noopener noreferrer">CVPR2017</a>), face representation and classification (<a href="https://ieeexplore.ieee.org/abstract/document/8255649/" target="_blank" rel="noopener noreferrer">TPAMI2018</a>,聽<a href="https://arxiv.org/abs/1603.07057" target="_blank" rel="noopener noreferrer">CVPR2016</a>). With this pipeline, we have achieved state-of-the-art performance on challenging IJB-A benchmark.</p> <p> </p> <h2>Unsupervised 3D Geometry Learning</h2> <p><a href="https://sites.usc.edu/iris-cvlab/files/2019/04/geometry_learning.png"><img loading="lazy" decoding="async" class="size-full wp-image-2138 aligncenter" src="https://sites.usc.edu/iris-cvlab/files/2019/04/geometry_learning.png" alt="" width="900" height="568" srcset="https://sites.usc.edu/iris-cvlab/files/2019/04/geometry_learning.png 900w, https://sites.usc.edu/iris-cvlab/files/2019/04/geometry_learning-300x189.png 300w, https://sites.usc.edu/iris-cvlab/files/2019/04/geometry_learning-768x485.png 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /></a></p> <p class="col-md-8">Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has attracted significant attention recently. It can be applied to many real-world applications, including autonomous driving, navigation and robotics. This task is challenging as estimating 3D geometry from monocular single image is an ill-posed problem and only unlabeled monocular videos are available for training. We propose to learn different 3D visual cues jointly in an unsupervised method. The framework takes monocular videos as training samples and estimates three 3D information (depth, surface normal, geometrical edges) on monocular single image. Details are presented in published papers, (<a href="https://arxiv.org/abs/1803.05648" target="_blank" rel="noopener noreferrer">CVPR2018</a>,聽<a href="https://arxiv.org/abs/1711.03665" target="_blank" rel="noopener noreferrer">AAAI2018</a>). We have achieved state-of-the-art performances on KITTI 2015 and Cityscapes datasets.</p> </div> </div> </div> </article> <article id="panel2" class="twentyseventeen-panel post-6 page type-page status-publish hentry" > <div class="panel-content"> <div class="wrap"> <header class="entry-header"> <h2 class="entry-title">Contact</h2> </header> <div class="entry-content"> <ul> <li><strong>Address</strong>: PHE 204, MC-2073, University of Southern California, Los Angeles, CA 90089-0273</li> <li><strong>Email</strong>: nevatia at usc.edu</li> </ul> <div class="agm_google_maps" id="map-a714c63618b30860e96bea4bd31fecac"></div><script type="text/javascript">_agmMaps.push({selector: "#map-a714c63618b30860e96bea4bd31fecac", data: {"markers":[{"title":"3737 Watt Way, Los Angeles, CA 90089, USA","body":"","icon":"https:\/\/sites.usc.edu\/iris-cvlab\/wp-content\/plugins\/wpmu_dev_maps_plugin\/\/img\/system\/marker.png","position":["34.0191533","-118.28877139999997"]}],"defaults":{"height":300,"width":300,"map_type":"ROADMAP","image_size":"small","image_limit":10,"map_alignment":"left","zoom":1,"units":"METRIC","map_api_key":"AIzaSyCZ9FZ0uKHIyalLPba2j-ddU9WerOrGGTE","snapping":0,"directions_snapping":1},"post_ids":[],"id":"1","title":"Location","height":"512","width":"512","zoom":"16","map_type":"ROADMAP","map_alignment":"left","show_map":1,"show_posts":0,"show_markers":0,"show_images":0,"image_size":null,"image_limit":0,"street_view":"0","street_view_pos":"0","street_view_pov":"0"}});</script> </div> </div> </div> </article> </main> </div> </div> <footer id="colophon" class="site-footer"> <div class="wrap"> <nav class="social-navigation" aria-label="Footer Social Links Menu"> <div class="menu-social-container"><ul id="menu-social" class="social-links-menu"><li id="menu-item-2010" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2010"><a href="https://www.facebook.com/usc"><span class="screen-reader-text">Facebook</span><svg class="icon icon-facebook" aria-hidden="true" role="img"> <use href="#icon-facebook" xlink:href="#icon-facebook"></use> </svg></a></li> <li id="menu-item-2011" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2011"><a href="https://twitter.com/usc"><span class="screen-reader-text">Twitter</span><svg class="icon icon-twitter" aria-hidden="true" role="img"> <use href="#icon-twitter" xlink:href="#icon-twitter"></use> </svg></a></li> <li id="menu-item-2012" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2012"><a href="https://www.instagram.com/uscedu/"><span class="screen-reader-text">Instagram</span><svg class="icon icon-instagram" aria-hidden="true" role="img"> <use href="#icon-instagram" xlink:href="#icon-instagram"></use> </svg></a></li> </ul></div> </nav> <div class="site-info"> <a href="https://wordpress.org/" class="imprint"> Proudly powered by WordPress </a> </div> </div> </footer> </div> </div> <div class="usc-branding-bar"> <div class="usc-branding-bar-container"> <div class="logo-wrapper"> <a class="logo" href="http://www.usc.edu/" tabindex="1"> <span class="screen-reader-text">University of Southern California Logo</span> </a> </div> </div> </div> <div class="usc-branding-footer-holder"> <div class="usc-branding-bar usc-branding-footer usc-branding-footer-copyright"> <div class="usc-branding-bar-container usc-branding-bar__footer-holder"> <span class="footer-school-name usc-branding-bar__school-name">University of Southern California</span> <nav class="usc-branding-bar__footer-nav" aria-label="USC global footer menu"> <ul> <li><a href="https://www.usc.edu/privacy-notice/" class="usc-branding-item-2881">Privacy Notice</a></li><li><a href="https://eeotix.usc.edu/notice-of-non-discrimination/" class="usc-branding-item-2882">Notice of Non-Discrimination</a></li><li><a href="https://accessibility.usc.edu/accessibility-at-usc/digital-accessibility/" class="usc-branding-item-2883">Digital Accessibility</a></li> </ul> </nav> </div> </div> </div> <style type="text/css"> .wmd-buttons-button, .wmd-buttons-content { display: inline-block; } .wmd-buttons-button { text-decoration:none !important; cursor:pointer; position: relative; text-decoration: none; letter-spacing:1px; margin:5px; } .wmd-buttons-button:hover { opacity:0.8; -moz-opacity: 0.8; -khtml-opacity: 0.8; -ms-filter: "progid:DXImageTransform.Microsoft.Alpha(Opacity=80)"; filter: alpha(opacity=80); } </style><script>window._agm = {"ajax_url":"https:\/\/sites.usc.edu\/iris-cvlab\/wp-admin\/admin-ajax.php","root_url":"https:\/\/sites.usc.edu\/iris-cvlab\/wp-content\/plugins\/wpmu_dev_maps_plugin\/","is_multisite":1,"libraries":[],"maps_api_key":"AIzaSyCZ9FZ0uKHIyalLPba2j-ddU9WerOrGGTE"};window.l10nStrings = {"close":"Close","get_directions":"Get Directions","geocoding_error":"There was an error geocoding your location. 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