Svetlana lazebnik scholar. The model is class-specific and allows an agent to .
- Svetlana lazebnik scholar 1007/s11263-005-3674-1 Corpus ID: 1330784; 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints @article{Rothganger20063DOM, title={3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints}, author={Fred Rothganger and Svetlana Lazebnik and Cordelia Schmid and Sep 5, 2010 · DOI: 10. : Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. , interest points equipped with characteristic scales. 1109/TPAMI. 2003. edu Julien Mairal Inria - Univ. Advances in Neural Information Processing Systems, 2018. She counts her students and colleagues as the reason why she remains so inspired in this line of work. edu Yin Li Assistant Professor, University of Wisconsin-Madison Verified email at wisc. , Lazebnik, S. fr David Forsyth Professor of Computer Science, University of Illinois, Urbana Champaign Verified email at uiuc. This paper presents Flickr30k Apr 11, 2017 · Image-language matching tasks have recently attracted a lot of attention in the computer vision field. 1007/978-3-319-10584-0_26 Corpus ID: 1346519; Multi-scale Orderless Pooling of Deep Convolutional Activation Features @inproceedings{Gong2014MultiscaleOP, title={Multi-scale Orderless Pooling of Deep Convolutional Activation Features}, author={Yunchao Gong and Liwei Wang and Ruiqi Guo and Svetlana Lazebnik}, booktitle={European Conference on Computer Vision}, year={2014}, url={https Lazebnik's University Scholar Honor Recognizes Her Research in Computer Vision Abdelzaher, Lazebnik and Rosu Named 2021 IEEE Fellows Eight CS Faculty and Students Receive Engineering, Campus Awards for Excellence Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. The model is class-specific and allows an agent to May 19, 2015 · This paper presents Flickr30K Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Semantic Scholar profile for Svetlana Lazebnik, with 3988 highly influential citations and 121 scientific research papers. 8, August 2005, pp. Names. 00685 Corpus ID: 109933186; Two Body Problem: Collaborative Visual Task Completion @article{Jain2019TwoBP, title={Two Body Problem: Collaborative Visual Task Completion}, author={Unnat Jain and Luca Weihs and Eric Kolve and Mohammad Rastegari and Svetlana Lazebnik and Ali Farhadi and Alexander G. This paper will analyze the activity of scene recognition starting with very simple methods i. Semantic Scholar profile for Liwei Wang, with 931 highly influential citations and 96 scientific research papers. 68 Corpus ID: 2421251; Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories @article{Lazebnik2006BeyondBO, title={Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories}, author={Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={2006 IEEE Computer Society Conference on Computer Vision Oct 13, 2003 · DOI: 10. Tighe, M. Our system is significantly better at localizing objects than other recent systems that predict bounding boxes from CNN features without object proposals. , Xu, K. CS Professor and Willett Faculty Scholar Svetlana Lazebnik was selected for contributions to computer vision. 541 Corpus ID: 9059202; Learning Deep Structure-Preserving Image-Text Embeddings @article{Wang2015LearningDS, title={Learning Deep Structure-Preserving Image-Text Embeddings}, author={Liwei Wang and Yin Li and Svetlana Lazebnik}, journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2015}, pages={5005-5013}, url={https://api 2017 Early Career Academic Achievement Alumni Award Svetlana Lazebnik. We formulate a simple rule Jun 18, 2003 · DOI: 10. Liwei Wang Yin Li Jing Huang Svetlana Lazebnik. org Jan 19, 2018 · DOI: 10. , Indyk, P. Jul 9, 2020 · DOI: 10. Narasimhan, S. Inspired by network pruning techniques, we exploit redundancies in large . Grenoble Alpes Verified email at inria. This chapter deals with the problem of whole-image categorization, inspired by psychophysical and psychological evidence that people can recognize scenes by considering them in a "holistic" manner, while overlooking most of the details of the constituent objects. Communications of the ACM 51, 117–122 (2008) Article Google Scholar Raginsky, M. The Laplacian detector extracts image Jun 20, 2011 · This paper proposes a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), which has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). Svetlana Lazebnik [a] (born 1979) [1] is a Ukrainian-American researcher in computer vision who works as a professor of computer science and Willett Faculty Scholar at the University of Illinois at Urbana–Champaign. These masks are learned in May 16, 2024 · Xiaoou’s expertise covered a wide spectrum of computer vision and image processing areas. Computer Science scholar academic profile. R-CNN is the only method that uses object proposals. & Saenko, K. Ponce, \A Sparse Texture Representation Using Local A ne Regions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. Schwing}, journal={ArXiv Apr 1, 2020 · DOI: 10. Image Parsing. Schmid, and J. 1211480 Corpus ID: 2046294; 3D object modeling and recognition using affine-invariant patches and multi-view spatial constraints @article{Rothganger20033DOM, title={3D object modeling and recognition using affine-invariant patches and multi-view spatial constraints}, author={Fred Rothganger and Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={2003 IEEE Lazebnik's University Scholar Honor Recognizes Her Research in Computer Vision Abdelzaher, Lazebnik and Rosu Named 2021 IEEE Fellows Eight CS Faculty and Students Receive Engineering, Campus Awards for Excellence Mar 7, 2014 · DOI: 10. J. The Flickr30k dataset has become a standard benchmark for sentence-based image description. 2016. edu Somali Chaterji Associate Professor, Purdue University Verified email at purdue. Help ensure that Illinois continues to set a global standard for engineering research and education. Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid: Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V. Ponce, \The Local Projective Shape of Smooth Surfaces and Their Outlines Sep 20, 2024 · Y. Accurately answering a question about a given image requires combining observations with general knowledge. This article presents a novel method for computing the visual hull of a solid bounded by a smooth surface and observed by a finite set of Nov 15, 2017 · This paper is able to add three fine-grained classification tasks to a single ImageNet-trained VGG-16 network and achieve accuracies close to those of separately trained networks for each task. Google Scholar. 193 Corpus ID: 2605321; Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval @article{Gong2013IterativeQA, title={Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval}, author={Yunchao Gong and Svetlana Lazebnik and Albert Gordo and Florent Perronnin}, journal={IEEE May 11, 2015 · One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. Ponce, \The Local Projective Shape of Smooth Surfaces and Their Outlines Andrew W. Jun 18, 2003 · DOI: 10. Guo, and S. International Conference on Computer Vision & Pattern Recognition (CVPR'03 … Sep 20, 2024 · My research specialty is computer vision. Research output: Contribution to journal › Article › peer-review S. We may want to classify a photograph based on a high-level Svetlana Lazebnik Full Professor, University of Illinois at Urbana-Champaign. Liwei Wang A. , interest points equipped with characteristic scales, using the Laplacian detector and the Harris detector for scale selection. She will describe two This section discusses the procedure for extracting scale-adapted local regions, i. The DPM system is the only baseline that does not use CNN features. DOI: 10. 2155-2167 13 p. Average Precision (AP) per category in the Pascal VOC 2007 test set. 222 Corpus ID: 261246012; Segmenting, modeling, and matching video clips containing multiple moving objects @article{Rothganger2004SegmentingMA, title={Segmenting, modeling, and matching video clips containing multiple moving objects}, author={Fred Rothganger and Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={Proceedings of the 2004 IEEE Computer Society May 11, 2015 · A simple rule of thumb is formulated to determine where auxiliary supervision branches after certain intermediate layers during training are added in order to train deeper networks. Abstract: This talk will survey some of Prof. Wang, R. Pandey and S. Suggest URL; Career & Education DOI: 10. Mar 7, 2014 · View a PDF of the paper titled Multi-scale Orderless Pooling of Deep Convolutional Activation Features, by Yunchao Gong and Liwei Wang and Ruiqi Guo and Svetlana Lazebnik View PDF Abstract: Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, adding layers makes training more difficult and computationally expensive. A. Article MATH Google Scholar Andoni, A. Niethammer, and S. , Sclaroff, S. Lazebnik, ECCV 2014; Scene Recognition and Weakly Supervised Object Localization with Deformable Part-Based Models M. tiny images and nearest neighbor classification and then it will examine techniques that resemble the state of the art, bags of quantized local features and classifying techniques like linear classifiers learned by Support Vector Machines (SVM). 1265-1278. An Associate Professor of Computer Science at Illinois, Svetlana Lazebnik explores research topics in computer vision, including the joint modeling of images and text, modeling and organizing large-scale photograph collections, object recognition, scene understanding, and machine learning techniques for visual recognition. Joined ; February 2019. edu Mar 1, 2006 · DOI: 10. edu Nov 22, 2017 · This paper proposes a concept weight branch that automatically assigns phrases to embeddings, whereas prior works predefine such assignments, which simplifies the representation requirements for individual embeds and allows the underrepresented concepts to take advantage of the shared representations before feeding them into concept-specific layers. This paper presents an approach for Corpus ID: 16001986; Building Rome on a Cloudless Day ( ECCV 2010 ) @inproceedings{Frahm2010BuildingRO, title={Building Rome on a Cloudless Day ( ECCV 2010 )}, author={Jan-Michael Frahm and Pierre Fite Georgel and David Gallup and Tim Johnson and Rahul and Raguram and Changchang Wu and Yi-Hung Jen and Enrique Dunn and Brian Clipp and Svetlana Lazebnik and Marc Pollefeys}, year={2010}, url The following articles are merged in Scholar. e. fr Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. This chapter deals with the problem of whole-image categorization. This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. edu Hervé Jégou Kyutai Verified email at kyutai. One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. While this is effortless for humans, reasoning with Dec 1, 2013 · DOI: 10. Lazebnik’s current work, with a focus on generative models for virtual try-on and person image stylization. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. edu. Jun 17, 2006 · DOI: 10. She will discuss Generative image models for virtual try-on and stylization. Lecture Notes in Computer Science 7576, Springer Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. We present an active detection model for localizing objects in scenes. This paper investigates two-branch neural networks for learning the similarity Speaker: Svetlana Lazebnik, Professor, Department of Computer Science, University of Illinois Talk Title: Generative image models for virtual try-on and stylization. com Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. tencent. 1007/978-3-030-58558-7_28 Corpus ID: 220424783; A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks @article{Jain2020ACS, title={A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks}, author={Unnat Jain and Luca Weihs and Eric Kolve and Ali Farhadi and Svetlana Lazebnik and Aniruddha Kembhavi and Alexander G. 1109/ICCV. 1007/978-3-030-58517-4_41 Corpus ID: 214775266; Memory-Efficient Incremental Learning Through Feature Adaptation @inproceedings{Iscen2020MemoryEfficientIL, title={Memory-Efficient Incremental Learning Through Feature Adaptation}, author={Ahmet Iscen and Jeffrey O. Schwing Svetlana Lazebnik In the years since Illinois CS professor Svetlana Lazebnik earned her PhD here in 2006, she has witnessed a great deal of change to her research field of computer vision. , matching a phrase to relevant regions. Numbers in bold are the second best result Jan 19, 2018 · This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. Scene Parsing with Object Instances and Occlusion Ordering J. Schwing and Aniruddha Kembhavi}, journal={2019 IEEE/CVF Conference on Research Scientist, Allen Institute for Artificial Intelligence - Cited by 4,006 - reinforcement learning - computer vision - algebraic statistics Svetlana Lazebnik. 1109/CVPR. The goal of human stylization is to transfer full-body human photos to a style specified by a single art Aug 15, 2007 · This article presents a novel method for computing the visual hull of a solid bounded by a smooth surface and observed by a finite set of cameras that only requires projective camera matrices or, equivalently, fundamental matrices for each pair of cameras. In order to train deeper networks, we propose to add auxiliary supervision branches after certain intermediate layers during training. 68 Corpus ID: 2421251; Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories @article{Lazebnik2006BeyondBO, title={Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories}, author={Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={2006 IEEE Computer Society Conference on Computer Vision University of Illinois at Urbana-Champaign - Cited by 37,069 - Computer vision - recognition Semantic Scholar profile for Jing Huang, with 76 highly influential citations and 9 scientific research papers. 1007/978-3-030-01225-0_5 Corpus ID: 3977226; Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights @inproceedings{Mallya2018PiggybackAA, title={Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights}, author={Arun Mallya and Dillon Davis and Svetlana Lazebnik}, booktitle={European Conference on Computer Vision}, year={2018}, url Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. Among the pioneering and influential examples, he significantly advanced the facial recognition system to the level that exceeded human capability when their GaussianFace system designed by him and collaborators, achieving a world-record accuracy of 98. DBLP. 2004. This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint Nov 19, 2015 · DOI: 10. These tasks include image-sentence matching, i. Zhang and Svetlana Lazebnik and Cordelia Schmid}, booktitle={European Conference on Computer Vision}, year={2020}, url Nov 18, 2015 · Table 1. The main themes of my research include scene understanding, joint modeling of images and text, large-scale photo collections, and machine learning techniques for visual recognition problems. By building upon ideas from network quantization and pruning, we learn binary masks that piggyback on an existing network, or are applied to unmodified weights of that network to provide good performance on a new task. 2006. org Jakob Verbeek FAIR, Meta Verified email at inria. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. Apr 14, 2023 · A novel skeleton deformation module is incorporated to reshape the pose of the input person and the DiOr pose-guided person generator is modified to be more robust to the rescaled poses falling outside the distribution of the realistic poses that the generator is originally trained on. Nov 18, 2015 · It is shown that agents guided by the proposed model are able to localize a single instance of an object after analyzing only between 11 and 25 regions in an image, and obtain the best detection results among systems that do not use object proposals for object localization. Schwing. : Locality sensitive binary codes from shift-invariant kernels. This section discusses the procedure for extracting scale-adapted local regions, i. 2012. 1238409 Corpus ID: 15208439; Affine-invariant local descriptors and neighborhood statistics for texture recognition @article{Lazebnik2003AffineinvariantLD, title={Affine-invariant local descriptors and neighborhood statistics for texture recognition}, author={Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={Proceedings Ninth IEEE International Conference Organizing the 'Scholars & Big Models: Unnat Jain, Iou-Jen Liu, Svetlana Lazebnik, Aniruddha Kembhavi, Luca Weihs*, Alexander Schwing* ICCV 2021 paper Yunchao Gong Liwei Wang Ruiqi Guo Svetlana Lazebnik. edu Dong Yu (俞栋) Distinguished Scientist @ Tencent AI Lab, ACM/IEEE/ISCA Fellow Verified email at global. Plummer, B. S. , Li, Y. Lazebnik and J. Lazebnik, C. Gong, L. 1211486 Corpus ID: 256365; A sparse texture representation using affine-invariant regions @article{Lazebnik2003AST, title={A sparse texture representation using affine-invariant regions}, author={Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. However, adding layers makes training more difficult and Svetlana Lazebnik; Published 2008; What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. Aug 1, 2005 · The proposed texture representation is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints. , Shih, K. Lazebnik, ICCV 2011. 1007/978-3-642-15561-1_27 Corpus ID: 2333407; Building Rome on a Cloudless Day @inproceedings{Frahm2010BuildingRO, title={Building Rome on a Cloudless Day}, author={Jan-Michael Frahm and Pierre Fite Georgel and David Gallup and Tim Johnson and Rahul Raguram and Changchang Wu and Yi-Hung Jen and Enrique Dunn and Brian Clipp and Svetlana Lazebnik}, booktitle={European Conference on Discover the latest information about Svetlana Lazebnik - D-Index & Metrics, Awards, Achievements, Best Publications and Frequent Co-Authors. [2] Illinois computer science professor Svetlana Lazebnik will be the Boston University Hariri Institute AIR Distinguished Speaker on Wednesday, April 3. Computer Science. edu Sicheng Mo University of California, Los Angeles Verified email at wisc. 27, no. 2019. Lazebnik, CVPR 2014 IJCV article S. Lazebnik, and A. , Apr 1 2022, In: IEEE transactions on pattern analysis and machine intelligence. 52% on the Labeled Faces in the Wild benchmark in 2014. Her research explores ways to understand images by recognizing and describing their content. M. edu Aniruddha Kembhavi Senior Director of Computer Vision, Allen Institute of Artificial Intelligence Verified email at allenai. — Scene recognition is one of the key features for Wednesday, April 3, 2024Speaker: Svetlana Lazebnik, Professor, Department of Computer Science, University of Illinois Talk Title: Generative image models for An entity graph is developed and a graph convolutional network is used to `reason' about the correct answer by jointly considering all entities and this leads to an improvement in accuracy of around 7% compared to the state of the art. , given an image query, retrieving relevant sentences and vice versa, and region-phrase matching or visual grounding, i. Over 1300 citations on Google Scholar. 44, 4, p. rxbt iwvjj csoy ovxnh ezvwppv ikxbpwvi gcx ykqkw wgjpm oqr
