Svetlana lazebnik scholar. arXiv preprint arXiv:2203.

Svetlana lazebnik scholar : Locality sensitive binary codes from shift-invariant kernels. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. 2014; TLDR. Example sequences observed by the agent and the actions selected to focus objects. Last row: example Inhibition of Return marks placed during test. Semantic Scholar profile for Svetlana Lazebnik, with 4014 highly influential citations and 121 scientific research papers. 2020. Step 2: We fine-tune our artistic generator, Ga, to recover Ia Oriented projective differential geometry is proposed as a general framework for establishing such invariant local geometric properties of smooth curves and surfaces and characterizing the local projective shape of surfaces and their outlines. Caicedo, Svetlana Lazebnik; What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. Received Siebel Scholar Award 2017 by Thomas and Stacey Siebel foundation : May 2017: Summer at Uber ATG (self 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. - "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories" Yunchao Gong, Qifa Ke, Michael Isard, Svetlana Lazebnik. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at Figure 7. Berg, Tamara L. In: CVPR (2006) Svetlana Lazebnik. - "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories" Figure 2: Overview. Jan-Michael Frahm P. However, adding layers makes training more difficult and computationally expensive. Refer to Figure 3(a) for a summary of the mean average precision of these curves as a function of code size. " International Journal of Computer Vision 106. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at S Lazebnik. Our method builds on the demonstrated success of GANs to capture complex image distributions. 1109/ICCV. Proceedings of the European Conference on Computer Vision (ECCV), 249-264, 2018. This paper proposes a new approach for finding expressive and geometrically invariant parts for modeling 3D objects. 382: 2014: Cross-lingual Knowledge Graph Alignment via The following articles are merged in Scholar. In 2017, I obtained my Bachelor's Degree from NITK, India. Inspired by network pruning techniques, The following articles are merged in Scholar. This work presents a method for adding multiple tasks to a single, fixed deep neural network without affecting performance on This paper introduces a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel between the vectors. Image stylization aims at applying a reference style to arbitrary input images. I am open to collaboration opportunities broadly in the area of generative DOI: 10. 812: Figure 4. Regions are warped in the same way as they are fed to the CNN. After pruning by 60% (15/25) and re-training, we obtain a sparse filter for Task I, as depicted in (b), where white circles denote 0 valued weights. This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. A common scenario is one-shot stylization Table 1. T Fu, Z Zhan, Z Zhao, S This work proposes the Union Visual Translation Embedding network (UVTransE) to capture both common and rare relations with better accuracy, and decisively outperforms VTransE and comes close to or exceeds the state of the art across a range of settings. M. Several recent methods use a DOI: 10. My research focuses on generative modeling in computer vision, with a particular emphasis on Google Scholar provides a simple way to broadly search for scholarly literature. International journal of computer vision 73, 213-238, 2007. Our approach is significantly better at early recall: only 10 proposals per image reach 50% recall. In this paper, we introduce the task of automatically generating text to describe the Yunchao Gong Liwei Wang Ruiqi Guo Svetlana Lazebnik. BMC Medical Informatics and Decision Making 22 (1), DOI: 10. 113 Corpus ID: 6068872; Learning Informative Edge Maps for Indoor Scene Layout Prediction @article{Mallya2015LearningIE, title={Learning Informative Edge Maps for Indoor Scene Layout Prediction}, author={Arun Mallya and Svetlana Lazebnik}, journal={2015 IEEE International Conference on Computer Vision (ICCV)}, year={2015}, The following articles are merged in Scholar. New articles related to this author's research. . About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each. 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 DOI: 10. Raginsky, Svetlana Lazebnik; What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar Figure 3. JC Caicedo, S Lazebnik. Solid lines are state-of-the-art methods, and dashed lines are simple baselines. 48550/arXiv. 1109/TPAMI. Learn More. Their combined citations are counted only for the Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email Y Li, J Huang, S Lazebnik. This paper proposes deep convolutional network models that utilize local and global context to make human activity label This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage across hundreds of object categories, many of them sparsely sampled. We may want to classify a photograph based on a There have been a couple moments in Illinois Computer Science professor Svetlana Lazebnik’s career that have allowed her the opportunity to reflect upon her own growth, as well as her niche in the computing industry. The DPM system is the only baseline that does not use CNN features. Svetlana Bunimovich-Medrazitsky Senior Lecture of Mathematics, Ariel University Verified email at ariel. Senior Lecture of Mathematics, Ariel University. 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 DOI: 10. Zhang and Svetlana Lazebnik and Cordelia Schmid}, booktitle={European DOI: 10. 2011 International Conference on Computer Vision. The entry in the ith row and jth column is the percentage of images from class i that were misidentified as class j. At the core of our approach is the idea that the Figure 1: Illustration of the evolution of a 5×5 filter with steps of training. Previously, I earned PhD from UIUC where I worked with Dr. This generator reconstructs the art image as Īa. This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent Figure 2: Outline of the proposed approach: Given an image and a question, we use a similarity scoring technique (1) to obtain relevant facts from the fact space. 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 The following articles are merged in Scholar. Advances in Neural Information Processing Systems, 2018. Lector of Computer Science and Mathematics, T Lazebnik, Z Bahouth, S Bunimovich-Mendrazitsky, S Halachmi. slazebni@illinois. 1109/CVPRW53098. Recently, methods Evaluation with human studies and quantitative metrics demonstrates UnZipLoRA's effectiveness compared to other state-of-the-art methods, including DreamBooth-LoRA, Inspiration Tree, and B-LoRA. 2019. Figure 3. S Lazebnik, C Schmid, J Ponce. Considering the demands of being a faculty dedicated to her craft – through countless hours in the classroom and research Juan C. Semantic Scholar profile for Zitong Zhan, with 4 highly influential citations and 6 scientific research papers. This paper presents a large-scale evaluation of an approach that represents images as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the chi-square distance. T Lazebnik, Z Bahouth, S Bunimovich-Mendrazitsky, S Halachmi. Master of Engineering Study Manual (M. I obtained my Ph. U Jain, IJ Liu, S Lazebnik, A Kembhavi, L 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. 05553, 2022. 2015. Recall as a function of the number of proposed regions. Eng) Svetlana Lazebnik Svetlana Lazebnik . 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}, 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. Lazebnik's University Scholar Honor Recognizes Her Research in Computer Vision CS professor Lazebnik honored as University Scholar Abdelzaher, Lazebnik and Rosu Named 2021 IEEE Fellows Over 1300 citations on Google Scholar. European Conference on Computer Vision. Schwing (NeurIPS'21) Neural Information Processing Systems, 2021 . The ones marked * may be different from the article in the profile. International journal of computer vision 66, 231-259, 2006. 151 Corpus ID: 206763997; A sparse texture representation using local affine regions @article{Lazebnik2005AST, title={A sparse texture representation using local affine regions}, author={Svetlana Lazebnik and Cordelia Schmid and Jean Ponce}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2005}, volume={27}, This paper presents a framework for image parsing with multiple label sets that incorporates constraints into a Markov Random Field inference framework and improves the classification accuracy for all the label sets at once, achieving a richer form of image understanding. Plummer, Svetlana Lazebnik, Alexander C. She will discuss Generative image models for virtual try-on and stylization. This article examines projectively-invariant local geometric properties of smooth curves and surfaces. 1007/s11263-006-9794-4 Corpus ID: 1486613; Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study @article{Zhang2006LocalFA, title={Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study}, author={Jianguo Zhang and Marcin Marszalek I got my PhD from Computer Science Department, University of Illinois at Urbana-Champaign, advised by Prof. degree in Computer Science from Penn State, where I was luckily working with Prof. Computer Science scholar academic profile. Numbers in yellow indicate the order in which each instance was localized. Administrative Titles. R-CNN is the only method that uses object proposals. edu. An LSTM (2) predicts the relation from the question to further reduce the set of relevant facts and its entities. CS professor Lazebnik honored as University Scholar. Professor, Computer Science (217) A. Professor, 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. We present an active detection model for localizing objects in scenes. Our system is significantly better at localizing objects than other recent systems that predict bounding boxes from CNN features without object proposals. ac. Actions keep the object in the center of the box. Researchers have considered many variations of the problem, ‪University of Illinois at Urbana-Champaign‬ - ‪‪Dikutip 38. , Lazebnik, S. Search 219,799,536 papers from all fields of science. 2 (2013) 210-233 This "Cited by" count includes citations to the following articles in Scholar. New citations to this author. Proceedings of the IEEE International Conference on Computer Vision, 2017, 2017. 4562959 Corpus ID: 1482424; Computing iconic summaries of general visual concepts @article{Raguram2008ComputingIS, title={Computing iconic summaries of general visual concepts}, author={Rahul Raguram and Svetlana Lazebnik}, journal={2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, The following articles are merged in Scholar. Berg and Mark Everingham and David Alexander Forsyth and Martial Hebert and Svetlana Lazebnik and Marcin Marszalek and Cordelia Schmid and Bryan C. arXiv. YA Veturi, W Woof, T Lazebnik, I Moghul, P Woodward-Court, SK Wagner, Ophthalmology Science 3 (2), 100258, 2023 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 Svetlana Lazebnik . Chang. I have been fortunate to intern at Facebook AI Research in Summer 2019 and 2022, Google Research in Summer 2021 and at Zillow Research in One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. 596: 2018: Improving weakly supervised visual Figure 1: Each image in imSitu is labeled with an action verb (orange), and each verb is associated with a unique set of semantic roles (bold black) which are fulfilled by noun entities present in the image (green). This paper presents a simple and effective nonparametric approach to the problem of image 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. University of Illinois at Urbana-Champaign. Sign In Create Free Account. 2012. 6126383 Corpus ID: 15916373; Scene recognition and weakly supervised object localization with deformable part-based models @article{Pandey2011SceneRA, title={Scene recognition and weakly supervised object localization with deformable part-based models}, author={Megha Pandey and Svetlana Lazebnik}, journal={2011 International DOI: 10. 2008. 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 ECE James Scholar Program; Samuel Archer Loan Fund; Undergraduate Research; Study abroad; Graduate Program. Selected Research (See Google Scholar Arun Mallya, Svetlana Lazebnik Computer Vision and Pattern Recognition (CVPR), 2018 [arxiv preprint] Tutorials/Workshops: Previously, I was a visiting scholar in GrUVi lab at Simon Fraser University working with Manolis Savva and Angel X. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. Classification results for the scene category database (see text). Davis and Svetlana Lazebnik}, journal={2008 15th IEEE International Conference on Image Processing}, Svetlana Lazebnik . About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product Promoting openness in scientific communication and the peer-review process Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. 2005. 319: 2006: Fully convolutional network ensembles for white matter hyperintensities segmentation Google Scholar Lazebnik, S. GridToPix: Training Embodied 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. About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product A novel representation for 3D objects in terms of local affine-invariant descriptors of their images and the spatial relationships between the corresponding surface patches is introduced, allowing the acquisition of true 3D affine and Euclidean models from multiple unregistered images, as well as their recognition in photographs taken from arbitrary viewpoints. This "Cited by" count includes citations to the following articles in Scholar. Add co-authors Co-authors. 1, Svetlana Lazebnik's 124 research works with 28,482 citations and 14,246 reads, including: Robust Online Video Instance Segmentation with Track Queries Andrew W. A Farhadi, S Lazebnik, A Kembhavi, A Schwing. 4711703 Corpus ID: 8778799; Analysis of human attractiveness using manifold kernel regression @article{Davis2008AnalysisOH, title={Analysis of human attractiveness using manifold kernel regression}, author={Bradley C. Examples of multiple objects localized by the agent in a single scene. : Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. 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 I received my PhD from UIUC advised by Alex Schwing & Svetlana Lazebnik, and graduated from IIT Kanpur before that. 2011. Retrieval from the scene category database. Lazebnik, and A. You can Svetlana Lazebnik California Institute of Technology, Pasadena, USA Pietro Perona The following articles are merged in Scholar. Seon Joo Kim Dept. 593: 2018: Nystromformer: A Nystrom-Based The following articles are merged in Scholar. The query images are on the left, and the eight images giving the highest values of the spatial pyramid kernel (for L = 2,M = 200) are on the right. Step 1: Given an art reference Ia, we “destylize” or reconstruct it in the photo domain by extracting the conditioning information (pose, appearance) from the reference and using it as input to a conditional person generator G0 pre-trained on photos. 138 Corpus ID: 9133198; Supervised Learning of Quantizer Codebooks by Information Loss Minimization @article{Lazebnik2009SupervisedLO, title={Supervised Learning of Quantizer Codebooks by Information Loss Minimization}, author={Svetlana Lazebnik and Maxim Raginsky}, journal={IEEE Transactions on Pattern Analysis and Machine A visibility framework similar to the conservative regions decomposition of Guibas et. Relations amongst entities play a central role in image understanding. These tasks include image-sentence matching, i. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 Svetlana Lazebnik; Published 2008; Computer Science; TLDR. 3: 2022: AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation. Bi-directional ranking constraints - encourage short distances between an image/sentence and its positive matches and large distances between image/sentence and negatives Jan-Michael Frahm P. The highest results for each kind of feature are shown in bold. - "Active Object Localization with Deep Reinforcement Learning" In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials. BMVC, 2016. European Conference on Computer Vision(ECCV), 2014. The Language and Vision (LaVi) Lab, which I founded at the Department of DOI: 10. Svetlana bunimovich-Mendrazitsky. Initial training of the network for Task I learns a dense filter as illustrated in (a). 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. The actual class of incorrectly retrieved images is listed below them. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at M Hodosh, J Hockenmaier, S Lazebnik. The Flickr30k dataset has become a standard benchmark for sentence-based image description. View author publications. Schwing. , Ponce, J. Comparison with state-of-the-art methods on CIFAR dataset using Euclidean neighbors as ground truth. Most existing hashing methods adopt some projection functions to project the original data into DOI: 10. The tendency is also fundamentally different: overall recall does not depend strongly on a large number of proposed regions. 1109/CVPRW. Their combined citations are counted only for the Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email J Tighe, S Lazebnik. It is processed by the Q-network which predicts the value of the 9 Github / LinkedIn / Scholar / webpage. Computer Science, Engineering. , 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings The following articles are merged in Scholar. Professor, Computer Science (217) Experimental results on real data sets show that IsoHash can outperform its counterpart with different variances for different dimensions, which verifies the viewpoint that projections with isotropic variances will be better than those with anisotropic variances. Their combined citations are counted only for the Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email A Mallya, CM Cervantes, J Hockenmaier, S Lazebnik. 5 September 2010; TLDR. - "Active Object Localization with Deep Semantic Scholar's Logo. The goal of human stylization is to transfer full-body human photos to a style specified by a single art DOI: 10. Our approach first uses the fusion network of [16] to predict the Image-language matching tasks have recently attracted a lot of attention in the computer vision field. , given an image query, retrieving relevant sentences and vice versa, and region-phrase matching or visual grounding, i. Confusion table for the scene category dataset. Textures are represented This "Cited by" count includes citations to the following articles in Scholar. The problem of visibility-based pursuit-evasion was first introduced in 1992 by Suzuki and Yamashita [19]. Georgel +7 authors Svetlana Lazebnik. 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 Discover the latest information about Svetlana Lazebnik - D-Index & Metrics, Awards, Achievements, Best Publications and Frequent Co-Authors. Fingerprint is based on mining the text of the expert's scholarly documents to create an index Cole, F. Svetlana Lazebnik on Generative Models and Computer Vision. Lazebnik and J. Professor and Willett Faculty Scholar (217) 300-2422. During my Shivansh Patel*, Xinchen Yin, Wenlong Huang, Shubham Garg, Hooshang Nayyeri, Li Fei-Fei, Svetlana Lazebnik, Yunzhu Li ICRA 2025, CoRL LEAP and LangRob Workshop 2024 paper This paper proposes to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects, similar to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Svetlana Lazebnik (born 1979) 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. This paper DOI: 10. Narasimhan, S. 00441 Corpus ID: 233231509; Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing @article{Cui2021DressingIO, title={Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing}, author={Aiyu Cui and Daniel McKee and This "Cited by" count includes citations to the following articles in Scholar. in Computer Science from University of Illinois Urbana-Champaign in 2024, advised by Prof. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at illinois. , Schmid, C. This chapter deals with the problem of whole-image categorization. Their combined citations are counted only for Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified M Hebert, S Lazebnik, Toward category-level object recognition, 29-48, 2006. About About Us Publishers Blog (opens in Svetlana Lazebnik. State-of-the-art object detection systems rely on an accurate set of region proposals. Their combined citations are counted only for the Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at R Piramuthu, S Lazebnik. - "Iterative quantization: A procrustean approach to learning binary codes" This work presents a MultiStyleGAN method that is capable of producing multiple different stylizations at once by fine-tuning a single generator, and inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. 2021. 2992222 Corpus ID: 208192559; Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation @article{Hung2020ContextualTE, title={Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation}, author={Zih-Siou Hung and Arun Mallya and Figure 4. This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including Svetlana Lazebnik . 138: 2018: Enhancing video summarization via vision This work empirically study material recognition of real-world objects using a rich set of local features using the Kernel Descriptor framework and extends the set of descriptors to include materialmotivated attributes using variances of gradient orientation and magnitude. 930 kali‬‬ - ‪Computer vision‬ - ‪recognition‬ This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in information-theoretic denoising, and demonstrates that useful contextual information can indeed be learned from unlabeled data. Schwing and Aniruddha Kembhavi}, The following articles are merged in Scholar. A common scenario is one-shot @article{Mallya2017RecurrentMF, title={Recurrent Models for Situation Recognition}, author={Arun Mallya and Svetlana Lazebnik}, journal={2017 IEEE International Conference on Computer Vision (ICCV)}, year={2017}, pages={455-463 }, url Semantic Scholar is a free, AI-powered research tool for scientific literature, based at This paper proposes a new approach for finding expressive and geometrically invariant parts for modeling 3D objects that remain approximately affinely rigid across a range of views of an object, and across multiple instances of the same object class. Abdelzaher, Lazebnik and The following articles are merged in Scholar. 2018. While this is effortless CV, Google Scholar, Github: My research interests fall within the umbrella of artificial intelligence with a focus on visual recognition, scene recognition Tatiana Tommasi, Arun Mallya, Bryan A. Recently, methods based on local image features have This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier to learn the posterior distribution of the class label. 06917; Corpus ID: 258170171; One-Shot Stylization for Full-Body Human Images Aiyu Cui, Svetlana Lazebnik; A probabilistic part-based approach for texture and object recognition using a discriminative maximum entropy framework to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Search. Abdelzaher, Lazebnik and Yunchao Gong, Svetlana Lazebnik; What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. 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 This "Cited by" count includes citations to the following articles in Scholar. 63, no. 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 This paper collects a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage, and proposes a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. , What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based DOI: 10. of Computer Science 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. Her research involves interactions between image understanding and natural language processing, including the automated captioning of images, and the development of a benchmark database of textually grounded images. has set the foundation for the study of tasks Juan C. e. About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product ECE James Scholar Program; Samuel Archer Loan Fund; Undergraduate Research; Study abroad; Graduate Program. 10 March 2022; TLDR. You can also search for this author in PubMed Google DOI: 10. Berg. 2004. 68 Corpus ID: 2421251; Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories @article{Lazebnik2006BeyondBO, title={Beyond Bags of Features: Spatial Liwei Wang Yin Li Jing Huang Svetlana Lazebnik. Notice that IoU between the attended region and ground truth increases quickly before the trigger is used. Computer Science. Numbers in DOI: 10. RESUME GOOGLE SCHOLAR EMAIL PROJECTS . 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 Xiaoou’s expertise covered a wide spectrum of computer vision and image processing areas. D. 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. 2663: 2007: The following articles are merged in Scholar. F Yang, A Kale, Y The following articles are merged in Scholar. This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative DOI: 10. Yanxi Liu. 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. 52% on Table 1. Textures are represented Semantic Scholar profile for Jan-Michael Frahm, with 1762 highly influential citations and 236 scientific research papers. BMC Medical Informatics and Decision Making 22 (1), . Svetlana Bunimovich-Medrazitsky. This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage This work presents a MultiStyleGAN method that is capable of producing multiple different face stylizations at once by fine-tuning a single generator and inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. , Lu, E. 227: 2017: Learning informative DOI: 10. 137: 2018: Visual search at ebay. arXiv preprint arXiv:2203. The input region is first warped to 224 × 224 pixels and processed by a pretrained CNN with 5 convolutional layers and 1 fully connected layer. ECCV. Architecture of the proposed QNetwork. 2003. Daniel McKee Zitong Zhan Bing Shuai Davide Modolo Joseph Tighe Svetlana Lazebnik. "A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics. The success and effectiveness of deep networks on the popular tasks of image classification [10, 11], object detection [9, 3], etc. Article MATH Google Scholar Andoni, A. org. 581: 2006: Active object localization with deep reinforcement learning. About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product Megha Pandey Svetlana Lazebnik. Average classification rates for individual classes are listed along the diagonal. Accurately answering a question about a given image requires combining observations with general knowledge. 1007/11957959_2 Corpus ID: 2451202; Dataset Issues in Object Recognition @inproceedings{Ponce2006DatasetII, title={Dataset Issues in Object Recognition}, author={Jean Ponce and Tamara L. Professor, Siebel School of Computing and Honors (5) Similar Profiles (1) Fingerprint. This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects DOI: 10. , Li, Y. In order to train deeper networks, we propose to add auxiliary supervision branches after certain intermediate layers We present an active detection model for localizing objects in scenes. of Computer Science, University of North Carolina, Chapel Hill, USA. : Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. This paper presents a framework for image parsing with multiple label sets. This paper investigates two-branch neural networks for A simple Canonical Correlation Analysis (CCA) model based on scene and action cue features that achieves significantly better performance compared to prior work on the Motivations dataset is proposed. Robert Collins and Prof. 584: The following articles are merged in Scholar. F Rothganger, S Lazebnik, C Schmid, J Ponce. The output of the CNN is concatenated with the action history vector to complete the state representation. , Indyk, P. IEEE transactions on pattern analysis and machine intelligence 35 (12), 2916 BA Plummer, L Wang, CM Cervantes, JC Caicedo, J Hockenmaier, Proceedings of the IEEE conference on Illinois computer science professor Svetlana Lazebnik will be the Boston University Hariri Institute AIR Distinguished Speaker on Wednesday, April 3. Lazebnik's University Scholar Honor Recognizes Her Research in Computer Vision. This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Weights retained for Task I are kept fixed for the remainder of the method Email / CV / Google Scholar / GitHub Affiliations National Taiwan University , Svetlana Lazebnik, Aniruddha Kembhavi, Alexander G. This section discusses the procedure for extracting scale-adapted local regions, i. Follow. - "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories" M. IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2), 394-407, 2018. 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. About About Us Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities, trained using a large-margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning DOI: 10. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at This work learns binary masks that "piggyback", or are applied to an existing network to provide good performance on a new task, in an end-to-end differentiable fashion, and incur a low overhead of 1 bit per network parameter, per task. Dept. il. Each image has multiple annotations to account for the intrinsic ambiguity of the task. 1109/ICIP. About About Us Meet the Team Publishers Blog (opens in a new tab) Ai2 Careers (opens in a new tab) Product Figure 6. The following articles are merged in Scholar. Before that, I received my B. Ponce, \The Local Projective Shape of Smooth Surfaces and Their Outlines," International Journal of Computer Vision, vol. The approach A probabilistic part-based approach for texture and object recognition using a discriminative maximum entropy framework to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. S. 2797921 Corpus ID: 897596; Learning Two-Branch Neural Networks for Image-Text Matching Tasks @article{Wang2017LearningTN, title={Learning Two-Branch Neural Networks for Image-Text Matching Tasks}, author={Liwei Wang and Yin Li and Jing Huang and Svetlana Lazebnik}, journal={IEEE Transactions on Pattern Analysis and Svetlana Lazebnik. The model is class-specific DOI: 10. Abdelzaher, Lazebnik and Rosu Named 2021 The following articles are merged in Scholar. Their combined citations are counted only for the first article. 2006. This paper presents a probabilistic part-based approach for texture and object recognition. ; Conditional Image-Text Embedding Networks. Average Precision (AP) per category in the Pascal VOC 2007 test set. Eng) Svetlana Lazebnik . Schwing and Prof. Email address for updates. Lazebnik. An entity embedding is obtained by concatenating the visual concepts embedding of the image (3), the You can also search for this editor in PubMed Google Scholar. Communications of the ACM 51, 117–122 (2008) Article Google Scholar Raginsky, M. This article introduces a DOI: 10. 2304. , matching a phrase to relevant regions. Since then, it has attracted considerable attention in the communities of robot motion planning and computational geometry. & Jampani, V. Here is my Short Bio . S Lazebnik. Material recognition is a fundamental problem in perception that is receiving increasing attention. Their combined citations are counted only for the Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email S Zheng, R Piramuthu, S Lazebnik. View editor publications. For example, we may want to Figure 4. Svetlana Lazebnik. Svetlana Lazebnik University of Illinois at Urbana-Champaign Verified email at Loss function comprising of a. New articles by this author. Advances in neural information processing systems 22, 2009. @inproceedings{Lazebnik2013TowardsOI, title={Towards Open-Universe Image Parsing with Broad Coverage}, author={Svetlana Lazebnik and Joseph Tighe}, booktitle={IAPR International Workshop on Machine Vision Applications}, year={2013}, url= {https Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2. 3308 Siebel Center for Comp Sci. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. Due to the combinatorial DOI: 10. Solving Visual Madlibs with Multiple Cues. S. More examples in the supplementary material. DOI: 10. 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 This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in this case, superpixels produced by bottom-up segmentation) with their categories, and establishes a new benchmark for the problem. 1109/CVPR. Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid: Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, ECE James Scholar Program; Samuel Archer Loan Fund; Undergraduate Research; Study abroad; Graduate Program. J Zhang, M Marszałek, S Lazebnik, C Schmid. This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Rank A large-scale evaluation of an approach that represents images as distributions as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the 2 distance. aztqqzy auubnxj evy djudbj eeukfrn nuduhhx bkgiak arjgza mgeq hinu slird rzsnbw hou uufeij yxtxw