A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer

Dense video captioning aims to localize and describe important events in untrimmed videos. Existing methods mainly tackle this task by exploiting only visual features, while completely neglecting the audio track. Only a few prior works have utilized both modalities, yet they show poor results or demonstrate the importance on a dataset with a specific domain. In this paper, we introduce Bi-modal Transformer which generalizes the Transformer architecture for a bi-modal input. We show the effectiveness of the proposed model with audio and visual modalities on the dense video captioning task, yet the module is capable of digesting any two modalities in a sequence-to-sequence task. We also show that the pre-trained bi-modal encoder as a part of the bi-modal transformer can be used as a feature extractor for a simple proposal generation module. The performance is demonstrated on a challenging ActivityNet Captions dataset where our model achieves outstanding performance. The code is available: v-iashin.github.io/bmtpublishedVersio

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

Multi-modal Video Content Understanding

Video is an important format of information. Humans use videos for a variety of purposes such as entertainment, education, communication, information sharing, and capturing memories. To this date, humankind accumulated a colossal amount of video material online which is freely available. Manual processing at this scale is simply impossible. To this end, many research efforts have been dedicated to the automatic processing of video content. At the same time, human perception of the world is multi-modal. A human uses multiple senses to understand the environment and objects, and their interactions. When watching a video, we perceive the content via both audio and visual modalities, and removing one of these modalities results in less immersive experience. Similarly, if information in both modalities does not correspond, it may create a sense of dissonance. Therefore, joint modelling of multiple modalities (such as audio, visual, and text) within one model is an active research area. In the last decade, the fields of automatic video understanding and multi-modal modelling have seen exceptional progress due to the ubiquitous success of deep learning models and, more recently, transformer-based architectures in particular. Our work draws on these advances and pushes the state-of-the-art of multi-modal video understanding forward. Applications of automatic multi-modal video processing are broad and exciting! For instance, the content-based textual description of a video (video captioning) may allow a visually- or auditory-impaired person to understand the content and, thus, engage in brighter social interactions. However, prior work in video content description relies on the visual input alone, missing vital information only available in the audio stream. To this end, we proposed two novel multi-modal transformer models that encode audio and visual interactions simultaneously. More specifically, first, we introduced a late-fusion multi-modal transformer that is highly modular and allows the processing of an arbitrary set of modalities. Second, an efficient bi-modal transformer was presented to encode audio-visual cues starting from the lower network layers allowing more rich audio-visual features and stronger performance as a result. Another application is the automatic visually-guided sound generation that might help professional sound (foley) designers who spend hours searching a database for relevant audio for a movie scene. Previous approaches for automatic conditional audio generation support only one class (e. g. “dog barking”), while real-life applications may require generation for hundreds of data classes and one would need to train one model for every data class which can be infeasible. To bridge this gap, we introduced a novel two-stage model that, first, efficiently encodes audio as a set of codebook vectors (i. e. trains to make “building blocks”) and, then, learns to sample these audio vectors given visual inputs to make a relevant audio track for this visual input. Moreover, we studied the automatic evaluation of the conditional audio generation model and proposed metrics that measure both quality and relevance of the generated samples. Finally, as video editing is becoming more common among non-professionals due to the increased popularity of such services as YouTube, automatic assistance during video editing grows in demand, e. g. off-sync detection between audio and visual tracks. Prior work in audio-visual synchronization was devoted to solving the task on lip-syncing datasets with “dense” signals, such as interviews and presentations. In such videos, synchronization cues occur “densely” across time, and it is enough to process just a few tens of a second to synchronize the tracks. In contrast, opendomain videos mostly have only “sparse” cues that occur just once in a seconds-long video clip (e. g. “chopping wood”). To address this, we: a) proposed a novel dataset with “sparse” sounds; b) designed a model which can efficiently encode seconds-long audio-visual tracks in a small set of “learnable selectors” that is, then, used for synchronization. In addition, we explored the temporal artefacts that common audio and video compression algorithms leave in data streams. To prevent a model from learning to rely on these artefacts, we introduced a list of recommendations on how to mitigate them. This thesis provides the details of the proposed methodologies as well as a comprehensive overview of advances in relevant fields of multi-modal video understanding. In addition, we provide a discussion of potential research directions that can bring significant contributions to the field