We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our model, we introduce a new dataset consisting of six episodes of the Big Bang Theory TV show and eighteen full movies covering different genres. Our experiments show that our multimodal model significantly outperforms several competitive baselines on the average weighted F-score metric. To demonstrate the effectiveness of our framework, we design an end-to-end memory network model that leverages our speaker naming model and achieves state-of-the-art results on the subtitles task of the MovieQA 2017 Challenge.
On the other hand, talking faces have been used to improve speaker recognition and diarization in TV shows @cite_28 @cite_17 @cite_29 . In the case of @cite_10 , they modeled the problem of speaker naming as facial recognition to identify speakers in news broadcasts. This work leveraged optical character recognition to read the broadcasters' names that were displayed on screen, requiring the faces to already be annotated.
@cite_28 @cite_29 @cite_10 @cite_17
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Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV movie live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online. We address the character identification problem in movies and television videos: assigning names to faces on the screen. Most prior work on person recognition in video assumes some supervised data such as screenplay or handlabeled faces. In this paper, our only source of ‘supervision’ are the dialog cues: first, second and third person references (such as “I'm Jack”, “Hey, Jack!” and “Jack left”). While this kind of supervision is sparse and indirect, we exploit multiple modalities and their interactions (appearance, dialog, mouth movement, synchrony, continuity-editing cues) to effectively resolve identities through local temporal grouping followed by global weakly supervised recognition. We propose a novel temporal grouping model that partitions face tracks across multiple shots while respecting appearance, geometric and film-editing cues and constraints. In this model, states represent partitions of the k most recent face tracks, and transitions represent compatibility of consecutive partitions. We present dynamic programming inference and discriminative learning for the model. The individual face tracks are subsequently assigned a name by learning a classifier from partial label constraints. The weakly supervised classifier incorporates multiple-instance constraints from dialog cues as well as soft grouping constraints from our temporal grouping. We evaluate both the temporal grouping and final character naming on several hours of TV and movies. We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any identity labels, using a form of cross-modal self-supervision; second, we develop a curriculum learning schedule for hard negative mining targeted to this task, that is essential for learning to proceed successfully; third, we demonstrate and evaluate cross-modal retrieval for identities unseen and unheard during training over a number of scenarios and establish a benchmark for this novel task; finally, we show an application of using the joint embedding for automatically retrieving and labelling characters in TV dramas. Naming faces is important for news videos browsing and indexing. Although some research efforts have been contributed to it, they only use the concurrent information between the face and name or employ some clues as features and use simple heuristic method or machine learning approach to finish the task. They use little extra knowledge about the names and faces. Different from previous work, in this paper we present a novel approach to name the faces by exploring extra knowledge obtained from image google. The behind assumption is that the faces of those important persons will turn out many times in the web images and could be retrieved from image google easily. Firstly, faces are detected in the video frames; and the name entities of candidate persons are extracted from the textual information by automatic speech recognition and close caption detection. Then, these candidate person names are used as queries to find the name related person images through image google. After that, the retrieved result is analyzed and some typical faces are selected through feature density estimation. Finally, the detected faces in the news video are matched with the faces selected from the result returned by image google to label each face. Experimental results on MSNBC news and CNN news demonstrate that the proposed approach is effective.