Timezone: »
Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.
Author Information
Sean Welleck (NYU)
Jialin Mao (New York University)
Kyunghyun Cho (NYU)
Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
Zheng Zhang (Shanghai New York Univeristy)
More from the Same Authors
-
2019 Workshop: Emergent Communication: Towards Natural Language »
Abhinav Gupta · Michael Noukhovitch · Cinjon Resnick · Natasha Jaques · Angelos Filos · Marie Ossenkopf · Angeliki Lazaridou · Jakob Foerster · Ryan Lowe · Douwe Kiela · Kyunghyun Cho -
2019 Workshop: Context and Compositionality in Biological and Artificial Neural Systems »
Javier Turek · Shailee Jain · Alexander Huth · Leila Wehbe · Emma Strubell · Alan Yuille · Tal Linzen · Christopher Honey · Kyunghyun Cho -
2019 Poster: Can Unconditional Language Models Recover Arbitrary Sentences? »
Nishant Subramani · Samuel Bowman · Kyunghyun Cho -
2019 Tutorial: Imitation Learning and its Application to Natural Language Generation »
Kyunghyun Cho · Hal Daumé III -
2018 Workshop: Emergent Communication Workshop »
Jakob Foerster · Angeliki Lazaridou · Ryan Lowe · Igor Mordatch · Douwe Kiela · Kyunghyun Cho -
2018 Poster: Loss Functions for Multiset Prediction »
Sean Welleck · Zixin Yao · Yu Gai · Jialin Mao · Zheng Zhang · Kyunghyun Cho -
2017 Workshop: Emergent Communication Workshop »
Jakob Foerster · Igor Mordatch · Angeliki Lazaridou · Kyunghyun Cho · Douwe Kiela · Pieter Abbeel -
2016 Poster: End-to-End Goal-Driven Web Navigation »
Rodrigo Nogueira · Kyunghyun Cho -
2016 Poster: Iterative Refinement of the Approximate Posterior for Directed Belief Networks »
R Devon Hjelm · Russ Salakhutdinov · Kyunghyun Cho · Nebojsa Jojic · Vince Calhoun · Junyoung Chung -
2015 Workshop: Multimodal Machine Learning »
Louis-Philippe Morency · Tadas Baltrusaitis · Aaron Courville · Kyunghyun Cho -
2015 Poster: Attention-Based Models for Speech Recognition »
Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio -
2015 Spotlight: Attention-Based Models for Speech Recognition »
Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Demonstration: Neural Machine Translation »
Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio -
2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio