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M2Lens provides explanations on intra- and inter-modal interactions at the global, subset, and local levels. In this paper, we present an interactive visual analytics system, M2Lens, to visualize and explain multimodal models for sentiment analysis. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and little research has been done on explaining multimodal models. It is not clear how models utilize multimodal information for sentiment predictions. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. It has become a vibrant and important research topic in natural language processing. Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. Finally, we give a brief conclusion about this survey and explore some open problems. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric.

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This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. There is no survey to conclude those emerging researches yet. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics.

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The separated audio samples and source code are available at. Furthermore, we observe that LASS-Net achieves promising generalization results when using diverse human-annotated descriptions as queries, indicating its potential use in real-world scenarios. Experimental results show that LASS-Net achieves considerable improvements over baseline methods.

We evaluate the performance of our proposed system with a dataset created from the AudioCaps dataset. To address this issue, we proposed LASS-Net, an end-to-end neural network that is learned to jointly process acoustic and linguistic information, and separate the target source that is consistent with the language query from an audio mixture. A unique challenge in LASS is associated with the complexity of natural language description and its relation with the audio sources. In this paper, we introduce the task of language-queried audio source separation (LASS), which aims to separate a target source from an audio mixture based on a natural language query of the target source (e.g., 'a man tells a joke followed by people laughing').

Informal feedback from first-time users suggests that our tools are easy to learn and greatly facilitate the process of editing raw footage into a final story. We have used our tools to create audio stories from a variety of raw speech sources, including scripted narratives, interviews and political speeches. Key features include a transcript-based speech editing tool that automatically propagates edits in the transcript text to the corresponding speech track a music browser that supports searching based on emotion, tempo, key, or timbral similar-ity to other songs and music retargeting tools that make it easy to combine sections of music with the speech. Our tools address several challenges in creating audio stories, including (1) navigating and editing speech, (2) selecting appropriate music for the score, and (3) editing the music to complement the speech. In contrast, we present a set of tools that analyze the audio content of the speech and music and thereby allow pro-ducers to work at much higher level. Ex-isting audio editing tools force story producers to manipu-late speech and music tracks via tedious, low-level waveform editing. Audio stories are an engaging form of communication that combine speech and music into compelling narratives.

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