Film media is a rich form of artistic expression. Unlike photography, and short videos, movies contain a storyline that is deliberately complex and intricate in order to engage its audience. In this paper we present a large scale study comparing the effectiveness of visual, audio, text, and metadata-based features for predicting high-level information about movies such as their genre or estimated budget. We demonstrate the usefulness of content-based methods in this domain in contrast to human-based and metadata-based predictions in the era of deep learning. Additionally, we provide a comprehensive study of temporal feature aggregation methods for representing video and text and find that simple pooling operations are effective in this domain. We also show to what extent different modalities are complementary to each other. To this end, we also introduce Moviescope, a new large-scale dataset of 5,000 movies with corresponding movie trailers (video + audio), movie posters (images), movie plots (text), and metadata.
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 mo…
Singapore University of Technology and Design（新加坡科技设计大学）；Goethe University Frankfurt（法兰克福歌德大学）
In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into account and incorporate the relation among multiple modalities by applying self-attention mechanism in a novel manner into the extracted features for emotion prediction. We compare it to the typically temporal integration of the self-attention based model, which in our case, allows to capture the relation of temporal representations of the movie while considering the sequential dependencies of emotion responses. We …
Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, as inspired by generative adversarial networks, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles …
International Institute of Information Technology Hyderabad（海得拉巴国际信息技术学院）
Video games have become an integral part of most people's lives in recent times. This led to an abundance of data related to video games being shared online. However, this comes with issues such as incorrect ratings, reviews or anything that is being shared. Recommendation systems are powerful tools that help users by providing them with meaningful recommendations. A straightforward approach would be to predict the scores of video games based on other information related to the game. It could be used as a means to validate user-submitted ratings as well as provide recommendations. This work pr…
Pre-training on large scale unlabelled datasets has shown impressive performance improvements in the fields of computer vision and natural language processing. Given the advent of large-scale instructional video datasets, a common strategy for pre-training video encoders is to use the accompanying speech as weak supervision. However, as speech is used to supervise the pre-training, it is never seen by the video encoder, which does not learn to process that modality. We address this drawback of current pre-training methods, which fail to exploit the rich cues in spoken language. Our proposal is…
Recognizing the emotional state of people is a basic but challenging task in video understanding. In this paper, we propose a new task in this field, named Pairwise Emotional Relationship Recognition (PERR). This task aims to recognize the emotional relationship between the two interactive characters in a given video clip. It is different from the traditional emotion and social relation recognition task. Varieties of information, consisting of character appearance, behaviors, facial emotions, dialogues, background music as well as subtitles contribute differently to the final results, which ma…
Fudan University（复旦大学）；Nanyang Technological University（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）
The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays. However, supervised methods face the obstacle of limited realistic training data. With this paper, we attempt to enhance data efficiency of the end-to-end alignment network NeuMATCH [15]. Recent research [56] suggests that network components dealing with different modalities may overfit and generalize at different speeds, creating difficulties for training. We propose to employ (1) layer-wise adaptive rate scaling (LARS) to align the magnitudes of gradient updates in…
Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities
SenseTime Research（商汤科技研究院）；The University of Sydney（悉尼大学）；University of Cologne（科隆大学）；EWI gGmbH（EWI有限责任公司）；Shanghai AI Laboratory（上海人工智能实验室）；Qing Yuan Research Institute of Shanghai Jiao Tong University（上海交通大学清源研究院）
Predicting Brain Responses To Natural Movies With Multimodal LLMs
Medical AI Research Center (MedARC)（医学人工智能研究中心（MedARC））；Psychological and Brain Sciences, Dartmouth College（心理学与脑科学系，达特茅斯学院）；Core for Advanced Magnetic Resonance Imaging (CAMRI), Baylor College of Medicine（先进磁共振成像核心（CAMRI），贝勒医学院）；Sophont；Princeton Neuroscience Institute（普林斯顿神经科学研究所）
Center for Research in Computer Vision (CRCV)（计算机视觉研究中心（CRCV））；University of Central Florida（中佛罗里达大学）；Netflix, Inc（奈飞公司）
With the development of media and networking technologies, multimedia applications ranging from feature presentation in a cinema setting to video on demand to interactive video conferencing are in great demand. Good synchronization between audio and video modalities is a key factor towards defining the quality of a multimedia presentation. The audio and visual signals of a multimedia presentation are commonly managed by independent workflows - they are often separately authored, processed, stored and even delivered to the playback system. This opens up the possibility of temporal misalignment …
École Normale Supérieure（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague（捷克布拉格技术大学捷克信息学、机器人学与控制论研究所）；Inria（法国国家信息与自动化研究所）；CIIRC（捷克信息学、机器人学与控制论研究所）
Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Toronto（多伦多大学）；Massachusetts Institute of Technology（麻省理工学院）；Inria（法国国家信息与自动化研究所）
True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions. We present a way to learn a compact multimodal feature representation that encodes all these modalities. Our model parameters are learned through a proxy task of inferring the temporal ordering of a set of unordered videos in a timeline. To this end, we create a new multimodal dataset for temporal ordering that consists of approximately 30K scenes (2-6 clips per scene) based on the "Large Scale Movie Description Challenge". We ana…
Contextual AD Narration with Interleaved Multimodal Sequence
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；ESAT, KU Leuven（鲁汶大学ESAT学院）；Ant Group（蚂蚁集团）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
INRIA（法国国家信息与自动化研究所）；Columbia University（哥伦比亚大学）；ENS / PSL Research University（巴黎高等师范学院/巴黎文理研究大学）；CNRS（法国国家科学研究中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
Suppose that we are given a set of videos, along with natural language descriptions in the form of multiple sentences (e.g., manual annotations, movie scripts, sport summaries etc.), and that these sentences appear in the same temporal order as their visual counterparts. We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time stamp for every sentence. Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities. We formulate this…
Active speaker detection in videos addresses associating a source face, visible in the video frames, with the underlying speech in the audio modality. The two primary sources of information to derive such a speech-face relationship are i) visual activity and its interaction with the speech signal and ii) co-occurrences of speakers' identities across modalities in the form of face and speech. The two approaches have their limitations: the audio-visual activity models get confused with other frequently occurring vocal activities, such as laughing and chewing, while the speakers' identity-based m…
