Chao-Yuan Wu

I am a Research Scientist at Facebook AI Research (FAIR), Menlo Park. My research interests are primarily in computer vision and machine learning, with a focus on video understanding. I did my Ph.D. in CS at UT Austin, where I was advised by Philipp Krähenbühl. I did my M.S. in ML at CMU, where I worked with Alex Smola.

cywu at fb dot com / CV / Google Scholar / Twitter

Publications





Towards Long-Form Video Understanding
Chao-Yuan Wu, Philipp Krähenbühl
CVPR, 2021
[paper] [project page & dataset] [code & models]



Memory Optimization for Deep Networks
Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl
ICLR, 2021 (Spotlight)
[paper] [code]




Lossless Image Compression through Super-Resolution
Sheng Cao, Chao-Yuan Wu, Philipp Krähenbühl
arXiv, 2020
[paper] [code & models]



A Multigrid Method for Efficiently Training Video Models
Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krähenbühl
CVPR, 2020 (Oral)
[paper] [code & models]




Fashion++: Minimal Edits for Outfit Improvement
Wei-Lin Hsiao, Isay Katsman*, Chao-Yuan Wu*, Devi Parikh, Kristen Grauman
ICCV, 2019
[paper] [code & models]
Media coverage: [Facebook AI Blog] [Vogue] [VentureBeat] [WIRED] [deeplearning.ai]



Long-Term Feature Banks for Detailed Video Understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick
CVPR, 2019 (Oral)
[paper] [code & models] [CVPR oral talk]



Video Compression through Image Interpolation
Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl
ECCV, 2018
[paper] [details] [code & models]



Compressed Video Action Recognition
Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J Smola, Philipp Krähenbühl
CVPR, 2018 (Spotlight)
[paper] [details] [code] [spotlight talk]


Sampling Matters in Deep Embedding Learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, Philipp Krähenbühl
ICCV, 2017
[paper] [details] [code] [code (third-party 1)] [code (third-party 2)]



Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization
Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S Dhillon, Pradeep Ravikumar
ICML, 2017


Recurrent Recommender Networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, How Jing
WSDM, 2017


Predicting Latent Structured Intents from Shopping Queries
Chao-Yuan Wu, Amr Ahmed, Gowtham Ramani Kumar, Ritendra Datta
WWW, 2017


Joint Training of Ratings and Reviews with Recurrent Recommender Networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola
ICLR, 2017 Workshop


Spectral Methods for Nonparametric Models
Hsiao-Yu Fish Tung, Chao-Yuan Wu, Manzil Zaheer, Alexander J Smola
arXiv preprint, 2017


Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J Smola
WWW, 2016 (Poster)


Using Navigation to Improve Recommendations in Real-time
Chao-Yuan Wu, Christopher V Alvino, Alexander J Smola, Justin Basilico
RecSys, 2016


Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS)
Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J Smola, Jing Jiang, Chong Wang
KDD, 2014