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

A Review: Movie Character Identification Based on Graph Matching

With the rapid development of movie and television industry a huge amount of movie and television data is being generated every day. To manage this data, efficient and effective technique is required, which understand the video contents and organize it properly, Character identification of movie is challenging problem due to huge variation in the appearance of each character and complex background, large motion, non-rigid deformation, occlusion, huge pose, expression, wearing, clothing, even makeup and hairstyle changes and other uncontrolled condition make the result of face detection and face tracking unreliable

From Benedict Cumberbatch to Sherlock Holmes: Character Identification in TV series without a Script

The goal of this paper is the automatic identification of characters in TV and feature film material. In contrast to standard approaches to this task, which rely on the weak supervision afforded by transcripts and subtitles, we propose a new method requiring only a cast list. This list is used to obtain images of actors from freely available sources on the web, providing a form of partial supervision for this task. In using images of actors to recognize characters, we make the following three contributions: (i) We demonstrate that an automated semi-supervised learning approach is able to adapt from the actor's face to the character's face, including the face context of the hair; (ii) By building voice models for every character, we provide a bridge between frontal faces (for which there is plenty of actor-level supervision) and profile (for which there is very little or none); and (iii) by combining face context and speaker identification, we are able to identify characters with partially occluded faces and extreme facial poses. Results are presented on the TV series 'Sherlock' and the feature film 'Casablanca'. We achieve the state-of-the-art on the Casablanca benchmark, surpassing previous methods that have used the stronger supervision available from transcripts

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table

A Labeling Task Design for Supporting Algorithmic Needs: Facilitating Worker Diversity and Reducing AI Bias

Studies on supervised machine learning (ML) recommend involving workers from various backgrounds in training dataset labeling to reduce algorithmic bias. Moreover, sophisticated tasks for categorizing objects in images are necessary to improve ML performance, further complicating micro-tasks. This study aims to develop a task design incorporating the fair participation of people, regardless of their specific backgrounds or task's difficulty. By collaborating with 75 labelers from diverse backgrounds for 3 months, we analyzed workers' log-data and relevant narratives to identify the task's hurdles and helpers. The findings revealed that workers' decision-making tendencies varied depending on their backgrounds. We found that the community that positively helps workers and the machine's feedback perceived by workers could make people easily engaged in works. Hence, ML's bias could be expectedly mitigated. Based on these findings, we suggest an extended human-in-the-loop approach that connects labelers, machines, and communities rather than isolating individual workers.Comment: 45 pages, 4 figure