Anchor Shot Detection with Deep Neural Network

Feng, Bailan; Bai, Jinfeng; Chen, Zhineng; Huang, Xiangsheng; Xu, Bo

doi:10.1007/978-3-319-13168-9_34

Cited by 1 publication

(1 citation statement)

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“…Some work have been done for visual shot role recognition. For example in [10] authors proposed a generalized anchor shot detector based on deep neural networks with a sampling strategy. They obtain interesting results on large-scale broadcast news videos (30 different TV channels).…”

Section: Related Workmentioning

confidence: 99%

Multimodal embedding fusion for robust speaker role recognition in video broadcast

Rouvier

Delecraz

Favre

et al. 2015

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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International audiencePerson role recognition in video broadcasts consists in classifying people into roles such as anchor, journalist, guest, etc. Existing approaches mostly consider one modality, either audio (speaker role recognition) or image (shot role recognition), firstly because of the non-synchrony between both modalities, and secondly because of the lack of a video corpus annotated in both modalities. Deep Neural Networks (DNN) approaches offer the ability to learn simultaneously feature representations (embeddings) and classification functions. This paper presents a multimodal fusion of audio, text and image embeddings spaces for speaker role recognition in asynchronous data. Monomodal embeddings are trained on exogenous data and fine-tuned using a DNN on 70 hours of French Broadcasts corpus for the target task. Experiments on the REPERE corpus show the benefit of the embeddings level fusion compared to the monomodal embeddings systems and to the standard late fusion method

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Section: Related Workmentioning

confidence: 99%