Discover and Learn New Objects from Documentaries

By Kai Chen, Hang Song, Chen Change Loy, Dahua Lin. 2017

21 citations recorded by the source corpus.

Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task. Detectors learned from a public dataset can only work with a fixed list of categories, while training from scratch usually requires a large amount of training data with detailed annotations. This work aims to explore a novel approach – learning object detectors from documentary films in a weakly supervised manner. This is inspired by the observation that documentaries often provide dedicated exposition of certain object categories, where visual presentations are aligned with subtitles. We believe that object detectors can be learned from such a rich source of information. Towards this goal, we develop a joint probabilistic framework, where individual pieces of information, including video frames and subtitles, are brought together via both visual and linguistic links. On top of this formulation, we further derive a weakly supervised learning algorithm, where object model learning and training set mining are unified in an optimization procedure. Experimental results on a real world dataset demonstrate that this is an effective approach to learning new object detectors.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

End-to-End Learning of Visual Representations From Uncurated Instructional Videos (2019, positive): “To avoid labeling visual data, several approaches have leveraged audio transcripts obtained from narrated videos using automatic speech recognition (ASR) as a way to supervise video models for object detection [4, 16, 56], captioning [34, 72], classification [3, 44, 49, 89], summarization [59] or re”