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Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned during training such as overwhel…
Neural Modular Control for Embodied Question Answering
Abhishek Das, Georgia Gkioxari, Stefan Lee +2
We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where t…
Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition
Jianwei Yang, Jiasen Lu, Stefan Lee +2
In an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work,…
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny +4
Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging fro…
Graph R-CNN for Scene Graph Generation
Jianwei Yang, Jiasen Lu, Stefan Lee +2
We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Re…
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Ashwin Kalyan, Stefan Lee, Anitha Kannan +1
Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of desc…