Audio-Visual Video Parsing (AVVP) entails the challenging task of localizing both uni-modal events (i.e., those occurring exclusively in either the visual or acoustic modality of a video) and multi-modal events (i.e., those occurring in both modalities concurrently). Moreover, the prohibitive cost of annotating training data with the class labels of all these events, along with their start and end times, imposes constraints on the scalability of AVVP techniques unless they can be trained in a weakly-supervised setting, where only modality-agnostic, video-level labels are available in the training data. To this end, recently proposed approaches seek to generate segment-level pseudo-labels to better guide model training. However, the absence of inter-segment dependencies when generating these pseudo-labels and the general bias towards predicting labels that are absent in a segment limit their performance. This work proposes a novel approach towards overcoming these weaknesses called Uncertainty-Weighted Weakly-Supervised Audio-Visual Video Parsing (UWAV). Additionally, our innovative approach factors in the uncertainty associated with these estimated pseudo-labels and incorporates a feature mixup based training regularization for improved training. Empirical results show that UWAV outperforms state-of-the-art methods for the AVVP task on multiple metrics, across two different datasets, attesting to its effectiveness and generalizability.1

Audio-visual navigation of an agent towards locating an audio goal is a challenging task especially when the audio is sporadic or the environment is noisy. In this paper, we present CAVEN, a Conversation-based Audio-Visual Embodied Navigation framework in which the agent may interact with a human/oracle for solving the task of navigating to an audio goal. Specifically, CAVEN is modeled as a budget-aware partially observable semi-Markov decision process that implicitly learns the uncertainty in the audio-based navigation policy to decide when and how the agent may interact with the oracle. Our CAVEN agent can engage in fully-bidirectional natural language conversations by producing relevant questions and interpret free-form, potentially noisy responses from the oracle based on the audio-visual context. To enable such a capability, CAVEN is equipped with: i) a trajectory forecasting network that is grounded in audio-visual cues to produce a potential trajectory to the estimated goal, and (ii) a natural language based question generation and reasoning network to pose an interactive question to the oracle or interpret the oracle's response to produce navigation instructions. To train the interactive modules, we present a large scale dataset: AVN-Instruct, based on the Landmark-RxR dataset. To substantiate the usefulness of conversations, we present experiments on the benchmark audio-goal task using the SoundSpaces simulator under various noisy settings. Our results reveal that our fully-conversational approach leads to nearly an order-of-magnitude improvement in success rate, especially in localizing new sound sources and against methods that use only uni-directional interaction.

Extensions of Neural Radiance Fields (NeRFs) to model dynamic scenes have enabled their near photo-realistic, free-viewpoint rendering. Although these methods have shown some potential in creating immersive experiences, two drawbacks limit their ubiquity: ( i) a significant reduction in reconstruction quality when the computing budget is limited, and (ii) a lack of semantic understanding of the underlying scenes. To address these issues, we introduce Gear-NeRF, which leverages semantic information from powerful image segmentation models. Our approach presents a principled way for learning a spatio-temporal (4D) semantic embedding, based on which we introduce the concept of gears to allow for stratified modeling of dynamic regions of the scene based on the extent of their motion. Such differentiation allows us to adjust the spatio- temporal sampling resolution for each region in proportion to its motion scale, achieving more photo-realistic dynamic novel view synthesis. At the same time, almost for free, our approach enables free-viewpoint tracking of objects of interest - a functionality not yet achieved by existing NeRF-based methods. Empirical studies validate the effectiveness of our method, where we achieve state-of-the-art rendering and tracking performance on multiple challenging datasets. The project page is available at: https://merl.com/research/highlights/gear-nerf

The distinct cough sounds produced by a variety of respiratory diseases suggest the potential for the development of a new class of audio bio-markers for the detection of COVID-19. Accurate audio biomarker-based COVID-19 tests would be inexpensive, readily scalable, and non-invasive. Audio biomarker screening could also be utilized in resource-limited settings prior to traditional diagnostic testing. Here we explore the possibility of leveraging three audio modalities: cough, breathing, and speech to determine COVID-19 status. We train a separate neural classification system on each modality, as well as a fused classification system on all three modalities together. Ablation studies are performed to understand the relationship between individual and collective performance of the modalities. Additionally, we analyze the extent to which temporal and spectral features contribute to COVID-19 status information contained in the audio signals.

There exists an unequivocal distinction between the sound produced by a static source and that produced by a moving one, especially when the source moves towards or away from the microphone. In this paper, we propose to use this connection between audio and visual dynamics for solving two challenging tasks simultaneously, namely: (i) separating audio sources from a mixture using visual cues, and (ii) predicting the 3D visual motion of a sounding source using its separated audio. Towards this end, we present Audio Separator and Motion Predictor (ASMP) -- a deep learning framework that leverages the 3D structure of the scene and the motion of sound sources for better audio source separation. At the heart of ASMP is a 2.5D scene graph capturing various objects in the video and their pseudo-3D spatial proximities. This graph is constructed by registering together 2.5D monocular depth predictions from the 2D video frames and associating the 2.5D scene regions with the outputs of an object detector applied on those frames. The ASMP task is then mathematically modeled as the joint problem of: (i) recursively segmenting the 2.5D scene graph into several sub-graphs, each associated with a constituent sound in the input audio mixture (which is then separated) and (ii) predicting the 3D motions of the corresponding sound sources from the separated audio. To empirically evaluate ASMP, we present experiments on two challenging audio-visual datasets, viz. Audio Separation in the Wild (ASIW) and Audio Visual Event (AVE). Our results demonstrate that ASMP achieves a clear improvement in source separation quality, outperforming prior works on both datasets, while also estimating the direction of motion of the sound sources better than other methods.

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. Our proposed Transformer variant uses a shuffling scheme on their multi-head outputs, demonstrating better regularization. To encode fine-grained visual information, we present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing spatio-semantic graph representations for every frame, and an inter-frame aggregation module capturing temporal cues. Our entire pipeline is trained end-to-end. We present experiments on the benchmark AVSD dataset, both on answer generation and selection tasks. Our results demonstrate state-of-the-art performances on all evaluation metrics.

Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high. Towards this end, we introduce Neural Uncertainty Quantifier (NUQ) - a stochastic quantification of the model’s predictive uncertainty, and use it to weigh the MSE loss. We propose a hierarchical, variational framework to derive NUQ in a principled manner using a deep, Bayesian graphical model. Our experiments on three benchmark stochastic video prediction datasets show that our proposed framework trains more effectively compared to the state-of-the-art models (especially when the training sets are small), while demonstrating better video generation quality and diversity against several evaluation metrics.

State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sources, especially when the same object class may produce varied sounds from distinct interactions. To address this challenging problem, we propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and segments this graph into subgraphs, each subgraph being associated with a unique sound obtained by co-segmenting the audio spectrogram. At its core, AVSGS uses a recursive neural network that emits mutually-orthogonal sub-graph embeddings of the visual graph using multi-head attention. These embeddings are used for conditioning an audio encoder-decoder towards source separation. Our pipeline is trained end-to-end via a self-supervised task consisting of separating audio sources using the visual graph from artificially mixed sounds.In this paper, we also introduce an “in the wild” video dataset for sound source separation that contains multiple non-musical sources, which we call Audio Separation in the Wild (ASIW). This dataset is adapted from the AudioCaps dataset, and provides a challenging, natural, and daily-life setting for source separation. Thorough experiments on the proposed ASIW and the standard MUSIC datasets demonstrate state-of-the-art sound separation performance of our method against recent prior approaches.

Person Re-Identification is the task of matching images of a person across multiple camera views. Almost all prior approaches address this challenge by attempting to learn the possible transformations that relate the different views of a person from a training corpora. Then, they utilize these transformation patterns for matching a query image to those in a gallery image bank at test time. This necessitates learning good feature representations of the images and having a robust feature matching technique. Deep learning approaches, such as Convolutional Neural Networks (CNN), simultaneously do both and have shown great promise recently. In this work, we propose two CNN-based architectures for Person Re-Identification. In the first, given a pair of images, we extract feature maps from these images via multiple stages of convolution and pooling. A novel inexact matching technique then matches pixels in the first representation with those of the second. Furthermore, we search across a wider region in the second representation for matching. Our novel matching technique allows us to tackle the challenges posed by large viewpoint variations, illumination changes or partial occlusions. Our approach shows a promising performance and requires only about half the parameters as a current state-of-the-art technique. Nonetheless, it also suffers from false matches at times. In order to mitigate this issue, we propose a fused architecture that combines our inexact matching pipeline with a state-of-the-art exact matching technique. We observe substantial gains with the fused model over the current state-of-the-art on multiple challenging datasets of varying sizes, with gains of up to about 21%.

Persuasive communication and interaction play an important and pervasive role in many aspects of our lives. With the rapid growth of social multimedia websites such as YouTube, it has become more important and useful to understand persuasiveness in the context of online social multimedia content. In this paper, we present our results of conducting various analyses of persuasiveness in speech with our multimedia corpus of 1,000 movie review videos obtained from ExpoTV.com, a popular social multimedia website. Our experiments firstly show that a speaker's level of persuasiveness can be predicted from acoustic characteristics and para-verbal cues related to speech fluency. Secondly, we show that taking acoustic cues in different time periods of a movie review can improve the performance of predicting a speaker's level of persuasiveness. Lastly, we show that a speaker's positive or negative attitude toward a topic influences the prediction performance as well.

Human communication involves conveying messages both through verbal and non-verbal channels (facial expression, gestures, prosody, etc.). Nonetheless, the task of learning these patterns for a computer by combining cues from multiple modalities is challenging because it requires effective representation of the signals and also taking into consideration the complex interactions between them. From the machine learning perspective this presents a two-fold challenge: a) Modeling the intermodal variations and dependencies; b) Representing the data using an apt number of features, such that the necessary patterns are captured but at the same time allaying concerns such as over-fitting. In this work we attempt to address these aspects of multimodal recognition, in the context of recognizing two essential speaker traits, namely passion and credibility of online movie reviewers. We propose a novel ensemble classification approach that combines two different perspectives on classifying multimodal data. Each of these perspectives attempts to independently address the two-fold challenge. In the first, we combine the features from multiple modalities but assume inter-modality conditional independence. In the other one, we explicitly capture the correlation between the modalities but in a space of few dimensions and explore a novel clustering based kernel similarity approach for recognition. Additionally, this work investigates a recent technique for encoding text data that captures semantic similarity of verbal content and preserves word-ordering. The experimental results on a recent public dataset shows significant improvement of our approach over multiple baselines. Finally, we also analyze the most discriminative elements of a speaker's non-verbal behavior that contribute to his/her perceived credibility/passionateness.

Conventional multimedia annotation/retrieval systems such as Normalized Continuous Relevance Model (NormCRM)[7] require a fully labeled training data for a good performance. Active Learning, by determining an order for labeling the training data, allows for a good performance even before the training data is fully annotated. In this work we propose an active learning algorithm, which combines a novel measure of sample uncertainty with a novel clustering-based approach for determining sample density and diversity and integrate it with NormCRM. The clusters are also iteratively refined to ensure both feature and label-level agreement among samples. We show that our approach outperforms multiple baselines both on a new, open dataset and on the popular TRECVID corpus at both the tasks of annotation and text-based retrieval of videos.

The ever expanding multimedia content (such as images and videos), especially on the web, necessitates effective text query-based search (or retrieval) systems. Popular approaches for addressing this issue, use the query-likelihood model which fails to capture the user's information needs. In this work therefore, we explore a new ranking approach in the context of image and video retrieval from text queries. Our approach assumes two separate underlying distributions for query and the document respectively. We then, determine the extent of similarity between these two statistical distributions for the task of ranking. Furthermore we extend our approach, using Active Learning techniques, to address the question of obtaining a good performance without requiring a fully labeled training dataset. This is done by taking Sample Uncertainty, Density and Diversity into account. Our experiments on the popular TRECVID corpus and the open, relatively small-sized USC SmartBody corpus show that we are almost at-par or sometimes better than multiple state-of-the-art baselines.

In this paper, we investigate the role of multiple context-based heart-rate variability descriptors for evaluating a person's psychological health, specifically anxiety disorders. The descriptors are extracted from visually sensed heart-rate signals obtained during the course of a semi-structured interview with a virtual human and can potentially integrate question context as well. The proposed descriptors are motivated by prior related work and are constructed based on histogram-based approaches, time and frequency domain analysis of heart-rate variability. In order to contextualize our descriptors, we use information about the polarity and intimacy levels of the questions asked. Our experiments reveal that the descriptors, both with and without context, perform far better than chance in predicting anxiety. Further on, we perform at-a-par with the state-of-the-art in predicting anxiety and other psychological disorders when we integrate the question context information into the descriptors.

The increasing prevalence of psychological distress disorders, such as depression and post-traumatic stress, necessitates a serious effort to create new tools and technologies to help with their diagnosis and treatment. In recent years, new computational approaches were proposed to objectively analyze patient non-verbal behaviors over the duration of the entire interaction between the patient and the clinician. In this paper, we go beyond non-verbal behaviors and propose a tri-modal approach which integrates verbal behaviors with acoustic and visual behaviors to analyze psychological distress during the course of the dyadic semi-structured interviews. Our approach exploits the advantages of the dyadic nature of these interactions to contextualize the participant responses based on the affective components (intimacy and polarity levels) of the questions. We validate our approach using one of the largest corpus of semi-structured interviews for distress assessment which consists of 154 multimodal dyadic interactions. Our results show significant improvement on distress prediction performance when integrating verbal behaviors with acoustic and visual behaviors. In addition, our analysis shows that contextualizing the responses improves the prediction performance, most significantly with positive and intimate questions.

Our lives are heavily influenced by persuasive communication, and it is essential in almost any types of social interactions from business negotiation to conversation with our friends and family. With the rapid growth of social multimedia websites, it is becoming ever more important and useful to understand persuasiveness in the context of social multimedia content online. In this paper, we introduce our newly created multimedia corpus of 1,000 movie review videos obtained from a social multimedia website called ExpoTV.com, which will be made freely available to the research community. Our research results presented here revolve around the following 3 main research hypotheses. Firstly, we show that computational descriptors derived from verbal and nonverbal behavior can be predictive of persuasiveness. We further show that combining descriptors from multiple communication modalities (audio, text and visual) improve the prediction performance compared to using those from single modality alone. Secondly, we investigate if having prior knowledge of a speaker expressing a positive or negative opinion helps better predict the speaker's persuasiveness. Lastly, we show that it is possible to make comparable prediction of persuasiveness by only looking at thin slices (shorter time windows) of a speaker's behavior.