Li Fei-Fei

TL;DR: Experimental results on hours of real world surveillance video and several Youtube videos show that the proposed algorithm could reliably locate the unusual events in the video sequence, outperforming the current state-of-the-art methods.

TL;DR: The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.

TL;DR: This work proposes a model that can scale to predict thousands of types of relationships from a few examples and improves on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship.

TL;DR: A Bayesian network formulation for relational shape matching is presented and the new Bethe free energy approach is used to estimate the pairwise correspondences between links of the template graphs and the data.

TL;DR: In this paper, a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and predicts socially plausible future by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss.