A
Alex Andonian
Researcher at Massachusetts Institute of Technology
Publications - 34
Citations - 2534
Alex Andonian is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Redundancy (engineering). The author has an hindex of 12, co-authored 27 publications receiving 1437 citations. Previous affiliations of Alex Andonian include Harvard University.
Papers
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Book ChapterDOI
Temporal Relational Reasoning in Videos
TL;DR: This paper introduces an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.
Journal ArticleDOI
Moments in Time Dataset: One Million Videos for Event Understanding
Mathew Monfort,Carl Vondrick,Aude Oliva,Alex Andonian,Bolei Zhou,Kandan Ramakrishnan,Sarah Adel Bargal,Tom Yan,Lisa M. Brown,Quanfu Fan,Dan Gutfreund +10 more
TL;DR: The Moments in Time dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds, can serve as a new challenge to develop models that scale to the level of complexity and abstract reasoning that a human processes on a daily basis.
Posted Content
Moments in Time Dataset: one million videos for event understanding
Mathew Monfort,Bolei Zhou,Sarah Adel Bargal,Alex Andonian,Tom Yan,Kandan Ramakrishnan,Lisa M. Brown,Quanfu Fan,Dan Gutfruend,Carl Vondrick,Aude Oliva +10 more
TL;DR: The Moments in Time dataset as mentioned in this paper is a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds, where each video is tagged with one action or activity label among 339 different classes.
Lore Goetschalckx, Alex Andonian, Aude Oliva, Phillip Isola: GANalyze: Toward Visual Definitions of Cognitive Image Properties.
TL;DR: In this article, a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability is introduced, where GANs allow to generate a manifold of natural-looking images with fine-grained differences in their visual attributes.
Proceedings ArticleDOI
GANalyze: Toward Visual Definitions of Cognitive Image Properties
TL;DR: A framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability is introduced and it is demonstrated that the same framework can be used to analyze image aesthetics and emotional valence.