Over the Spring semester, I participated in the NYC Media Lab’s Synthetic Media challenge, in collaboration with CHANEL. This challenge brought together teams of faculty and students from a variety
CategoryResearch
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Authors Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré Abstract Weak supervision is a popular method for building machine learning models without relying
Multi-Resolution Weak Supervision for Sequential Data
Authors Frederic Sala, Paroma Varma, Jason Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James R. Priest, Christopher Ré Abstract Since manually labeling training
Scene Graph Prediction with Limited Labels
Authors Vincent Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, Li Fei-Fei Image: Our semi-supervised method automatically generates probabilistic relationship labels to train any scene graph model. Abstract Visual
Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction
Authors Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, Li Fei-Fei Image: We introduce a scene graph approach that formulates predicates as learned functions, which result in an embedding space
View-Dependent Video Textures for 360° Video
Authors Sean J. Liu, Maneesh Agrawala, Stephen DiVerdi, Aaron Hertzmann Image: In 360◦ video, viewers can look anywhere at any time. In the opening scene of Invasion!, a rabbit emerges
AI-based Request Augmentation to Increase Crowdsourcing Participation
Authors Junwon Park, Ranjay Krishna, Pranav Khadpe, Li Fei-Fei, Michael Bernstein Abstract To support the massive data requirements of modern supervised machine learning (ML) algorithms, crowdsourcing systems match volunteer contributors
Video Event Specification using Programmatic Composition
Authors Daniel Y. Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, Kayvon Fatahalian Abstract Many real-world video analysis applications require the
Text-based Editing of Talking-head Video
Authors O. Fried, A. Tewari, M. Zollhöfer, A. Finkelstein, E. Shechtman, D. B Goldman, K. Genova, Z. Jin, C. Theobalt and M. Agrawala Image: We propose a novel text-based editing