Andre Araujo

Andre Araujo

Research Scientist @ Google DeepMind

Publications

You can also find my articles on my Google Scholar profile.

  • K. Maninis*, K. Chen*, S. Ghosh*, A. Karpur*, K. Chen, Y. Xia, B. Cao, D. Salz, G. Han, J. Dlabal, D. Gnanapragasam, M. Seyedhosseini, H. Zhou, A. Araujo, “TIPS: Text-Image Pretraining with Spatial awareness,” arXiv:2410.16512, Oct. 2024 [Paper]

  • N. Ypsilantis, K. Chen, A. Araujo, O. Chum, “UDON: Universal Dynamic Online distillatioN for generic image representations,” Proc. NeurIPS, Dec. 2024 [Paper]

  • H. Jiang, A. Karpur, B. Cao, Q. Huang, A. Araujo, “OmniGlue: Generalizable Feature Matching with Foundation Model Guidance,” Proc. CVPR'24, Jun. 2024 [Paper] [Code]

  • G. Potje, F. Cadar, A. Araujo, R. Martins, E. Nascimento, “XFeat: Accelerated Features for Lightweight Image Matching,” Proc. CVPR'24, Jun. 2024 [Paper] [Code]

  • L. Castrejon, T. Mensink, H. Zhou, V. Ferrari, A. Araujo, J. Uijlings, “HAMMR: HierArchical MultiModal React agents for generic VQA,” arXiv:2404.05465, Apr. 2024 [Paper]

  • A. Karpur, G. Perrotta, R. Martin-Brualla, H. Zhou, A. Araujo, “LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals,” Proc. 3DV'24 (oral), Mar. 2024 [Paper]

  • V. Jampani*, K. Maninis*, A. Engelhardt, A. Karpur, K. Truong, K. Sargent, S. Popov, A. Araujo, R. Martin-Brualla, K. Patel, D. Vlasic, V. Ferrari, A. Makadia, C. Liu, Y. Li, H. Zhou, “NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations,” Proc. NeurIPS, Dec. 2023 [Paper]

  • E. Ramzi, N. Audebert, C. Rambour, A. Araujo, X. Bitot, N. Thome, “Optimization of Rank Losses for Image Retrieval,” arXiv:2309.08250, Sep. 2023 [Paper]

  • N. Ypsilantis, K. Chen, B. Cao, M. Lipovský, P. Dogan-Schönberger, G. Makosa, B. Bluntschli, M. Seyedhosseini, O. Chum, A. Araujo, “Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations,” Proc. ICCV'23, Oct. 2023 [Paper]

  • S. Shao*, K. Chen, A. Karpur, Q. Cui, A. Araujo, B. Cao*, “Global Features are All You Need for Image Retrieval and Reranking,” Proc. ICCV'23, Oct. 2023 [Paper]

  • T. Mensink*, J. Uijlings*, L. Castrejon, A. Goel, F. Cadar, H. Zhou, F. Sha, A. Araujo, V. Ferrari, “Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories,” Proc. ICCV'23, Oct. 2023 [Paper]

  • D. Aiger, A. Araujo, S. Lynen, “Yes, we CANN: Constrained Approximate Nearest Neighbors for local feature-based visual localization,” Proc. ICCV'23, Oct. 2023 [Paper]

  • G. Potje, F. Cadar, A. Araujo, R. Martins, E. Nascimento, “Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints,” Proc. CVPR'23, Jun. 2023 [Paper] [Project]

  • A. Iscen, A. Araujo, B. Gong, C. Schmid, “Class-Balanced Distillation for Long-Tailed Visual Recognition,” Proc. BMVC, Nov. 2021 [Paper]

  • Z. Kim, A. Araujo, B. Cao, C. Askew, J. Sim, M. Green, N. F. Tilla, T. Weyand, “Towards A Fairer Landmark Recognition Dataset,” arxiv:2108.08874, Aug. 2021 [Paper]

  • B. Cao*, A. Araujo*, J. Sim, “Unifying Deep Local and Global Features for Image Search,” Proc. ECCV'20, Aug. 2020 [Paper] [Code]

  • T. Weyand*, A. Araujo*, B. Cao, J. Sim, “Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval” Proc. CVPR (oral), Jun. 2020 [Paper] [Dataset] [Code]

  • A. Araujo, W. Norris, J. Sim, “Computing Receptive Fields of Convolutional Neural Networks,” Distill, Nov. 2019 [Paper]

  • M. Teichmann*, A. Araujo*, M. Zhu, J. Sim, “Detect-to-Retrieve: Efficient Regional Aggregation for Image Search,” Proc. CVPR, Jun. 2019 [Paper] [Poster]

  • J.-B. Boin, A. Araujo, B. Girod, “Recurrent Neural Networks for Person Re-identification Revisited,” Proc. MIPR, Mar. 2019 (Best Paper Award) [Paper] [Slides]

  • H. Noh, A. Araujo, J. Sim, T. Weyand, B. Han, “Large-Scale Image Retrieval with Attentive Deep Local Features,” Proc. ICCV, Oct. 2017 [Paper] [Poster] [Code]

  • J.-B. Boin, A. Araujo, L. Ballan and B. Girod, “Effective Fisher Vector Aggregation for 3D Object Retrieval,” Proc. ICASSP, Mar. 2017 [Paper]

  • A. Araujo and B. Girod, “Large-Scale Video Retrieval Using Image Queries,” Transactions on Circuits and Systems for Video Technology (TCSVT), Feb. 2017 [Paper] [Code]

  • A. Araujo, H. Lakshman, R. Angst and B. Girod, “Modeling the Impact of Keypoint Detection Errors on Local Descriptor Similarity,” Proc. ICIP, Sept. 2016 [Paper] [Supplemental Material] [Slides]

  • A. Araujo, “Large-Scale Video Retrieval Using Image Queries,” Ph.D. Thesis, Stanford University, Jun. 2016 [Thesis] [Slides] [Defense]

  • A. Araujo, J. Chaves, H. Lakshman, R. Angst and B. Girod, “Large-Scale Query-by-Image Video Retrieval Using Bloom Filters,” arXiv:1604.07939, Apr. 2016 [Paper] [SI2V/VB Datasets] [ClassX Dataset] [Slideshare Dataset]

  • A. Araujo, J. Chaves, R. Angst and B. Girod, “Temporal Aggregation for Large-Scale Query-by-Image Video Retrieval,” Proc. ICIP, Sept. 2015 [Paper] [Poster] [Code]

  • A. Araujo, J. Chaves, D. Chen, R. Angst and B. Girod, “Stanford I2V: A News Video Dataset for Query-by-Image Experiments,” Proc. ACM Multimedia Systems, Mar. 2015 [Paper] [Slides] [Stanford I2V Dataset] [Code]

  • A. Araujo, D. Chen, P. Vajda and B. Girod, “Real-time Query-by-Image Video Search System,” Proc. ACM MM, Nov. 2014 [Paper] [Poster] [Video]

  • A. Araujo, M. Makar, V. Chandrasekhar, D. Chen, S. Tsai, H. Chen, R. Angst and B. Girod, “Efficient Video Search Using Image Queries,” Proc. ICIP, Oct. 2014 [Paper] [Poster] [CNN2h Dataset]

  • J. Koyama, M. Makar, A. Araujo and B. Girod, “Interframe Compression with Selective Update Framework of Local Features for Mobile Augmented Reality,” Proc. ICME Workshops, Jul. 2014 [Paper]

  • D. Chen, M. Makar, A. Araujo and B. Girod, “Interframe Coding of Global Image Signatures for Mobile Augmented Reality,” Proc. DCC, Mar. 2014 (Capocelli Prize for Best Student Paper) [Paper] [Slides]

  • M. Yu, P. Vajda, D. Chen, M. Daneshi, S. Tsai, A. Araujo, H. Chen and B. Girod, “EigenNews: A Personalized News Delivery Video Platform,” Proc. ACM MM, Oct. 2013 [Paper] [Video]

  • D. Chen, P. Vajda, S. Tsai, M. Daneshi, M. Yu, H. Chen, A. Araujo and B. Girod, “Analysis of Visual Similarity in News Videos with Robust and Memory-Efficient Image Retrieval,” Proc. ICME Workshops, Jul. 2013 [Paper] [Slides]

  • M. Daneshi, P. Vajda, D. Chen, S. Tsai, M. Yu, H. Chen, A. Araujo and B. Girod, “EigenNews: Generating and Delivering Personalized News Videos,” Proc. ICME Workshops, Jul. 2013 [Paper]

  • A. Araujo, F. Silveira, H. Lakshman, J. Zepeda, A. Sheth, P. Perez and B. Girod, “The Stanford/Technicolor/Fraunhofer HHI Video Semantic Indexing,” Proc. TRECVID, Nov. 2012 [Paper]

  • A. Araujo, “Tag-Sensitive Features for Large-Scale Classification,” Stanford Technical Report, Dec. 2011 [Paper]

  • M. Makar, Y.-C. Lin, A. Araujo and B. Girod, “Compression of VQM Features for Low Bit-Rate Video Quality Monitoring,” Proc. IEEE Workshop on Multimedia Signal Processing (MMSP), Oct. 2011 (Top 10% Paper Award) [Paper]

  • A. Araujo, P. Weinzaepfel, P. Perez and C. Diot, “Object Bank-based Scene Classification,” Technicolor Technical Report, Sept. 2011 [Paper]

  • A. Araujo and S. Pancoast, “Logo Detection in High Motion Sports Videos,” Stanford Technical Report, Jun. 2011 [Paper]

  • A. Araujo, M. Daneshi and R. Peng, “Entropy Constrained Overcomplete-based Coding of Natural Images,” Stanford Technical Report, Mar. 2011 [Paper]