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DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Justin Johnson, Andrej Karpathy, Li Fei-Fei
We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning ta…
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori +1
In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the proce…
Visual7W: Grounded Question Answering in Images
Yuke Zhu, Oliver Groth, Michael Bernstein +1
We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the…
Detecting events and key actors in multi-person videos
Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija +3
Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose…
SentenceRacer: A Game with a Purpose for Image Sentence Annotation
Kenji Hata, Sherman Leung, Ranjay Krishna +2
Recently datasets that contain sentence descriptions of images have enabled models that can automatically generate image captions. However, collecting these datasets are still very…
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin +3
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multip…