Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering

Abstract

This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each. We use multiple instance learning to handle the lack of supervision on the level of individual person instances, and weighted loss to handle unbalanced training data. Further, we show how specialized features trained on these datasets can be used to improve accuracy on the Visual Question Answering (VQA) task, in the form of multiple choice fill-in-the-blank questions (Visual Madlibs). Specifically, we tackle two types of questions on person activity and person-object relationship and show improvements over generic features trained on the ImageNet classification task.

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
• Analyzing the Performance of Multilayer Neural Networks for Object Recognition
• Revisiting Visual Question Answering Baselines
• Describing Common Human Visual Actions in Images
• Fine-grained Activity Recognition with Holistic and Pose based Features