486 citations · 965 across the 16 of their papers we have counts for
7 papers · 1 filter
Deep Bayesian Active Learning for Multiple Correct Outputs
Khaled Jedoui, Ranjay Krishna, Michael Bernstein +1
Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive. The assumption that these tasks…
Establishing an Evaluation Metric to Quantify Climate Change Image Realism
Sharon Zhou, Alexandra Luccioni, Gautier Cosne +2
With success on controlled tasks, generative models are being increasingly applied to humanitarian applications [1,2]. In this paper, we focus on the evaluation of a conditional ge…
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction
Apoorva Dornadula, Austin Narcomey, Ranjay Krishna +2
Scene graph prediction --- classifying the set of objects and predicates in a visual scene --- requires substantial training data. However, most predicates only occur a handful of…
Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
Snehalkumar, S. Gaikwad, Durim Morina +35
Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores,…
Scene Graph Prediction with Limited Labels
Vincent S. Chen, Paroma Varma, Ranjay Krishna +3
Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete…
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna +3
Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pr…