My research is in computational vision, in particular how to integrate mechanisms for visual recognition and perceptual organization with 3D scene understanding. We apply these computational models to a variety of problems including development of tools for biological image and shape analysis in neuroscience, animal development and palynology as well as applications to forensic science.

some research highlights:

• Y. Zhao, H. Ma, S. Kong, C. Fowlkes, "Instance Tracking in 3D Scenes from Egocentric Videos", Technical Report, (Dec. 2023) arXiv:2312.04117
  
  
• S. Shafique, B. Kong, S. Kong, C. Fowlkes, "Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos", WACV (Jan. 2023) arXiv:2205.02361
  
  
• Y. Zhao, C. Barnes, Y. Zhou, E. Shechtman, S. Amirghodsi, C. Fowlkes, "GeoFill: Reference-Based Image Inpainting of Scenes with Better Geometric Understanding", WACV (Jan. 2023) arXiv:2201.08131
  
  
• Z. Wang, J. Yang, C. Fowlkes, "The Best of Both Worlds: Combining Model-Based and Non-parametric Approaches for 3D Human Body Estimation", CVPR-W ABAW, (June 2022), arXiv:2205.00508
  
  
• M. Wallingford, H. Li, A. Achille, A. Ravichandran, C. Fowlkes, R. Bhotika, S. Soatto, "Task Adaptive Parameter Sharing for Multi-Task Learning", CVPR, (June 2022), arXiv:2203.16708
  
  
• Z. Li, A. Ravichandran, C. Fowlkes, M. Polito, R. Bhotika, S. Soatto "Representation Consolidation for Training Expert Students", Technical Report (July 2021), arXiv:2107.08039
  
  
• Y. Zhao, S. Kong, C. Fowlkes, "Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias", CVPR, (June 2021) arXiv:2007.03887
  
  
• Z. Cai, A. Ravichandran, S. Maji, C. Fowlkes, Z. Tu, S. Soatto "Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning", CVPR, (June 2021) arXiv:2101.08482
  
  
• I. Romero, S. Kong, C. Fowlkes, C. Jaramillo, M. Urban, F. Oboh-Ikuenobe, C. D'Apolito, S. Punyasena, "Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy", Proc. of the National Academy of Sciences (PNAS) Oct 2020, 202007324. DOI:10.1073/pnas.2007324117
  
  
• H. Kashyap, C. Fowlkes, J. Krichmar, "Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes", IEEE TNNLS, 10.1109/TNNLS.2020.3006467 [pdf]
  
  
• Y. Zhao, S. Kong, D. Shin, C. Fowlkes, "Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation", CVPR, (June 2020) [pdf] [project] arXiv:2002.12114
  
  
• C. Huh, K. Abdelaal, K. Salinas, D. Gu, J. Zeitoun, D. Velez, J. Peach, C. Fowlkes, S. Gandhi, "Long-term monocular deprivation during juvenile critical period disrupts binocular integration in mouse visual thalamus", Journal of Neuroscience, (Nov. 2019) 10.1523/JNEUROSCI.1626-19.2019
  
  
• D. Shin, Z. Ren, E. Sudderth, C. Fowlkes, "3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers", ICCV (Oct. 2019) [pdf] [project] arXiv:1902.06729
  
  
• P. Nguyen, D. Ramanan, C. Fowlkes, "Weakly-supervised Action Localization with Background Modeling" ICCV (Oct. 2019) [pdf] arXiv:1908.06552
  
  
• A. Achille, M. Lam, R. Tewari, A. Ravichandran, S. Maji, C. Fowlkes, S. Soatto, P. Perona, "Task2Vec: Task Embedding for Meta-Learning", ICCV (Oct. 2019) [pdf] arXiv:1902.03545
  
  
• Z. Wang, L. Chen, S. Rathore, D. Shin, C. Fowlkes, "Geometric Pose Affordance : 3D Human Pose with Scene Constraints", (May 2019). arXiv:1905.07718
  
  
• S. Kong, C. Fowlkes, "Multigrid Predictive Filter Flow for Unsupervised Learning on Videos", (April 2019). arXiv:1904.01693
  
  
• P. Rajendran, R. Challis, C. Fowlkes, P. Hanna, J. Tompkins, M. Jordan, S. Hiyari, B. Gabris-Weber, A. Greenbaum, K. Chan, B. Deverman, H. Munzberg, J. Ardell, G. Salama, V. Gradinaru, K. Shivkumar, "Identification of peripheral neural circuits that regulate heart rate using optogenetic and viral vector strategies", Nature Communications 10:1944 (April 2019)
  
  
• P. Nguyen, C. Fowlkes, D. Ramanan "Active Testing: An Efficient and Robust Framework for Estimating Accuracy", ICML, (July 2018) [pdf]
  
  
• S. Kong, C. Fowlkes, "Recurrent Pixel Embedding for Instance Grouping", CVPR (June 2018) arXiv:1712.08273 [pdf]
  
  
• D. Shin, C. Fowlkes, D. Hoiem, "Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction", CVPR, (June 2018) [pdf]
  
  
• M. Lee, C. Fowlkes, "Spacetime Localization and Mapping", ICCV, Venice, (October 2017). [pdf]
  
  
  
  full list of publications here...

students:

current academic advisees and lab members

• Daeyun Shin (PhD)
  
• Samia Shafique (PhD)
  
• Yunhan Zhao (PhD)
  

past academic advisees

• Viktor Liviniuk (MS, 2022) now at Nuro
  
• Alina Liviniuk (MS, 2022) now at Pinterest
  
• Zhe Wang (PhD, 2021) now at Cruise
  
• Chinmayee Athalye (MS, 2020)
  
• Linfeng Wang (MS, 2020)
  
• Shaurya Rathore (MS, 2020)
  
• Ryan Sivoraphonh (BS, 2020)
  
• Shu Kong (PhD, 2019) now at CMU
  
• Minhaeng Lee (PhD, 2019) now at Apple
  
• Liyan Chen (BS, 2019) now at UT Austin
  
• Bailey Kong (PhD, 2018)
  
• Phuc Nguyen (PhD, 2018) now at Google
  
• Dongxu Zhao (MS 2018) now at UNC Chapel Hill
  
• Jasmine Bhanushali (MS, 2018) now at Microsoft
  
• Maharshi Patel (BS, 2017) now at Microsoft
  
• Golnaz Ghiasi (PhD, 2016) now at Google Research
  
• Raúl Díaz (PhD, 2016) now at Amazon
  
• Sam Hallman (PhD, 2015) now at Amazon
  
• Gio Borje (BS, 2015) now at LinkedIn
  
• Samreen Anjum (MS, 2014) now at Qatar Computing Research Institute
  
• Shaofei Wang (MS, 2013) now at U Penn
  
• Jason Lee (BS, 2013) now at OCLARO
  
• Julian Yarkony (PhD,2012) now at Experian Data Lab
  
• Matthew Nease (BS, 2012) now at Edmunds
  
• Shu-Chi Hsu (MS, 2012)
  
• Ragib Morshed (MS, 2011) now at Amazon
  
• Nityananda Jayadevaprakash (MS, 2010) now at Apple
  
• Sangeeta Jha (MS, 2010) now at Intel
  
• Tony Tran (MS, 2009) now at Uber
  
• Dennis Park (MS, 2009) now at Vicarious
  

past co-advisees and visitors

• Michelle Ngo (MCBS rotation, 2018)
  
• Katiana Khouri (MCBS rotation, 2017)
  
• Yoon Jung (BS, 2017)
  
• Michael Chiang (PhD w/Olivier Cinquin, 2015)
  
• Joseph Nash (CCBS rotation, 2015)
  
• Hyeoungho Bae (PhD w/Pai Chu, 2013) now at Microsoft
  
• Yihang Bo (visiting PhD, 2010-2011) now at Beijing Film Academy
  

awards:

• UCI Chancellors fellow (2019-2022)
  
  
• American Staistical Society SPAIG award for building a statistically sound and scientifically solid foundation for the analysis and interpretation of forensic evidence.
  
  
• Helmholtz Prize in 2015 for significant impact in computer vision with our paper:
  
  "A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics", D. Martin, C. Fowlkes, D. Tal, J. Malik. ICCV 2001 [pdf]
  
  
• Award for Teaching Excellence in Undergarduate Education, UCI ICS, 2014

• National Science Foundation CAREER Award, 2013

• Marr Prize in 2009 for our paper:
  
  "Discriminative models for multi-class object layout", C. Desai, D. Ramanan, C. Fowlkes. ICCV 2009 [pdf]
  
  

teaching:

cs 116 : computational photography and vision [w10,w11,w13,w14,w16,w17,w18,w19,w20,w21,w22]
cs 117 : project in computer vision [w09,f13,s15,s17,s18,s19,s20,s21,s22]
cs 216 : image understanding [f08,f10,f11,f12,s14,w16,s17,s18,s20,s21,s22]
cs 217 : light and geometry in vision [s10,w12,s15,s19,s21]
ics 162 : modeling and world building [f12,s14,f15]
cs 295 : research topics in vision [s09,w10,w11]
cs 177 : applications of probability in computer science [s08]

funding:

My research is currently supported by:

• NSF ( IIS-1813785, CNS-1730158 )
• NIH ( OD OT2:OD023848, )
• NIST CSAFE: Center for Statistics and Applications in Forensic Evidence
• Gift from Qualcomm Research
• Gift from Adobe

previously:

• NIH ( NIBIB R25:EB022366, OD OT2:OD024899, NIA R01AG057748 )
• National Science Foundation ( DBI-1053036, DBI-1262547, IIS-1618806, IIS-1253538 )
• Gift from Adobe
• Hardware donation from NVIDIA
• Google Research Award
• UC Labs Research Program

service and meetings/workshops organized:

• Area chair for CVPR (2010,2012,2016, 2017), ICCV (2015,2017), ECCV (2016,2018), ISMB (2016)

• Associate editor for IEEE TPAMI (2018-), CVIU (2016-), IEEE/ACM TCBB (2015-2018)

• USA-Sino Summer School in Vision, Learning, Pattern Recognition (VLPR 2012) Fudan Unviersity, Shanghai, China July 2012.
  
  
• The Eighth Workshop on Perceptual Organization in Computer Vision (POCV 2012), CVPR, Providence, RI.
  
  
• Southern California Computer Vision Meetup (2009-2012)
  
• Big Data Image Processing and Analysis (2016-2018).