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Learning Object Representations with Predicate Functions: Enabling Few-Shot Scene Graph Prediction

Published in arXiv: Computer Science: Computer Vision and Pattern Recognition, 2019

This paper introduces a novel few-shot capable Scene Graph Prediction model, which applies Graph Convolution Networks to learn a relationship-oriented embedding space for objects in the scene.

Recommended citation: Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, Li Fei-Fei. "Learning Object Representationswith Predicate Functions: Enabling Few-Shot Scene Graph Prediction." arXiv. 2019. https://arxiv.org/abs/1906.04876

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

Published in Advances in Neural Information Processing Systems, 2019

This paper introduces a novel crowdsourcing framework to scalably and accurately evaluate human perception of generative ML models. HYPE is a more direct measurement of human judgement than automated proxies, and it is cheaper and more consistent than other human evaluations.

Recommended citation: Sharon Zhou*, Mitchell Gordon*, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael Bernstein. "HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models." Advances in Neural Information Processing Systems. 2019. http://papers.nips.cc/paper/8605-hype-a-benchmark-for-human-eye-perceptual-evaluation-of-generative-models

Austin Narcomey

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Learning Object Representations with Predicate Functions: Enabling Few-Shot Scene Graph Prediction

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

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