21 research outputs found
Deep Multimodal Speaker Naming
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
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
Speaker identification refers to the task of localizing the face of a person
who has the same identity as the ongoing voice in a video. This task not only
requires collective perception over both visual and auditory signals, the
robustness to handle severe quality degradations and unconstrained content
variations are also indispensable. In this paper, we describe a novel
multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies
both visual and auditory modalities from the beginning of each sequence input.
The key idea is to extend the conventional LSTM by not only sharing weights
across time steps, but also sharing weights across modalities. We show that
modeling the temporal dependency across face and voice can significantly
improve the robustness to content quality degradations and variations. We also
found that our multimodal LSTM is robustness to distractors, namely the
non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory
dataset and showed that our system outperforms the state-of-the-art systems in
speaker identification with lower false alarm rate and higher recognition
accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16
Story-based Video Retrieval in TV series using Plot Synopses
We present a novel approach to search for plots in the story-line of structured videos such as TV series. To this end, we propose to align natural language descriptions of the videos, such as plot synopses, with the corresponding shots in the video. Guided by subtitles and person identities the align-ment problem is formulated as an optimization task over all possible assignments and solved efficiently using dynamic programming. We evaluate our approach on a novel dataset comprising of the complete season 5 of Buffy the Vampire Slayer, and show good alignment performance and the abil-ity to retrieve plots in the storyline