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preprint2020arXiv

Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks

Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provide consumers with freely changeable viewports and a larger field of view covering the $360^{\circ}\times180^{\circ}$ spherical surface, which makes the objective quality assessment of omnidirectional images more challenging. In this paper, motivated by the characteristics of the human vision system (HVS) and the viewing process of omnidirectional contents, we propose a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA). Generally, observers tend to give the subjective rating of a 360-degree image after passing and aggregating different viewports information when browsing the spherical scenery. Therefore, in order to model the mutual dependency of viewports in the omnidirectional image, we build a spatial viewport graph. Specifically, the graph nodes are first defined with selected viewports with higher probabilities to be seen, which is inspired by the HVS that human beings are more sensitive to structural i

preprint2020arXiv

NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks

This paper presents NANCY, a system that generates adaptive bit rates (ABR) for video and adaptive network coding rates (ANCR) using reinforcement learning (RL) for video distribution over wireless networks. NANCY trains a neural network model with rewards formulated as quality of experience (QoE) metrics. It performs joint optimization in order to select: (i) adaptive bit rates for future video chunks to counter variations in available bandwidth and (ii) adaptive network coding rates to encode the video chunk slices to counter packet losses in wireless networks. We present the design and implementation of NANCY, and evaluate its performance compared to state-of-the-art video rate adaptation algorithms including Pensieve and robustMPC. Our results show that NANCY provides 29.91% and 60.34% higher average QoE than Pensieve and robustMPC, respectively.

preprint2020arXiv

Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding

Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of a queried object according to the given sentence. Currently, most existing grounding methods are restricted to well-aligned segment-sentence pairs. In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sentences. This challenging task requires to capture critical object relations to identify the queried target. However, existing approaches cannot distinguish notable objects and remain in ineffective relation modeling between unnecessary objects. Thus, we propose a novel object-aware multi-branch relation network for object-aware relation discovery. Concretely, we first devise multiple branches to develop object-aware region modeling, where each branch focuses on a crucial object mentioned in the sentence. We then propose multi-branch relation reasoning to capture critical object relationships between the main branch and auxiliary branches. Moreover, we apply a diversity loss to make each branch only pay attention to its corresponding object and boost multi-branch learning. The extensive experiments show the effectiveness of our proposed method.

preprint2020arXiv

Fine granularity access in interactive compression of 360-degree images based on rate-adaptive channel codes

In this paper, we propose a new interactive compression scheme for omnidirectional images. This requires two characteristics: efficient compression of data, to lower the storage cost, and random access ability to extract part of the compressed stream requested by the user (for reducing the transmission rate). For efficient compression, data needs to be predicted by a series of references that have been pre-defined and compressed. This contrasts with the spirit of random accessibility. We propose a solution for this problem based on incremental codes implemented by rate-adaptive channel codes. This scheme encodes the image while adapting to any user request and leads to an efficient coding that is flexible in extracting data depending on the available information at the decoder. Therefore, only the information that is needed to be displayed at the user's side is transmitted during the user's request, as if the request was already known at the encoder. The experimental results demonstrate that our coder obtains a better transmission rate than the state-of-the-art tile-based methods at a small cost in storage. Moreover, the transmission rate grows gradually with the size of th

preprint2020arXiv

Multi-modal Cooking Workflow Construction for Food Recipes

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense reasoning. However, existing efforts rely on hand-crafted features to extract the workflow graph from recipes due to the lack of large-scale labeled datasets. Moreover, they fail to utilize the cooking images, which constitute an important part of food recipes. In this paper, we build MM-ReS, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps are multi-modal, featuring both text instructions and cooking images. We then propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow, which achieved over 20% performance gain over existing hand-crafted baselines.

preprint2020arXiv

A Coverage-Aware Resource Provisioning Method for Network Slicing

With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs. This problem leads to a Mixed Integer Linear Programming formulation. A two-step approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into accoun

preprint2020arXiv

Assessing the Quality-of-Experience of Adaptive Bitrate Video Streaming

The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models. Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices. We collect human opinions for each video with a series of carefully designed subjective experiments. Subsequent data analysis and testing/comparison of ABR algorithms and QoE models using the database lead to a series of novel observations and interesting findings, in terms of the effectiveness of subjective experiment methodologies, the interactions between user experience and source content, viewing device and encoder type, the heterogeneities in the bias and preference of user experiences, the behaviors of ABR algorithms, and the performance of objective QoE models. Most importantly, our results suggest that a better objective QoE model, or a better understanding of human perceptual experience and behaviour, is the most dominating factor in improving the per

preprint2020arXiv

Graph Neural Networks for 3D Multi-Object Tracking

3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work often uses a tracking-by-detection pipeline, where the feature of each object is extracted independently to compute an affinity matrix. Then, the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this pipeline is to learn discriminative features for different objects in order to reduce confusion during data association. To that end, we propose two innovative techniques: (1) instead of obtaining the features for each object independently, we propose a novel feature interaction mechanism by introducing Graph Neural Networks; (2) instead of obtaining the features from either 2D or 3D space as in prior work, we propose a novel joint feature extractor to learn appearance and motion features from 2D and 3D space. Through experiments on the KITTI dataset, our proposed method achieves state-of-the-art 3D MOT performance. Our project website is at http://www.xinshuoweng.com/projects/GNN3DMOT.

preprint2020arXiv

Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space

Both images and music can convey rich semantics and are widely used to induce specific emotions. Matching images and music with similar emotions might help to make emotion perceptions more vivid and stronger. Existing emotion-based image and music matching methods either employ limited categorical emotion states which cannot well reflect the complexity and subtlety of emotions, or train the matching model using an impractical multi-stage pipeline. In this paper, we study end-to-end matching between image and music based on emotions in the continuous valence-arousal (VA) space. First, we construct a large-scale dataset, termed Image-Music-Emotion-Matching-Net (IMEMNet), with over 140K image-music pairs. Second, we propose cross-modal deep continuous metric learning (CDCML) to learn a shared latent embedding space which preserves the cross-modal similarity relationship in the continuous matching space. Finally, we refine the embedding space by further preserving the single-modal emotion relationship in the VA spaces of both images and music. The metric learning in the embedding space and task regression in the label space are jointly optimized for both cross-modal matching and single

preprint2020arXiv

MMM : Exploring Conditional Multi-Track Music Generation with the Transformer

We propose the Multi-Track Music Machine (MMM), a generative system based on the Transformer architecture that is capable of generating multi-track music. In contrast to previous work, which represents musical material as a single time-ordered sequence, where the musical events corresponding to different tracks are interleaved, we create a time-ordered sequence of musical events for each track and concatenate several tracks into a single sequence. This takes advantage of the Transformer's attention-mechanism, which can adeptly handle long-term dependencies. We explore how various representations can offer the user a high degree of control at generation time, providing an interactive demo that accommodates track-level and bar-level inpainting, and offers control over track instrumentation and note density.

preprint2020arXiv

DTDN: Dual-task De-raining Network

Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition. It needs to address two mutually exclusive objectives: removing rain streaks and reserving realistic details. Balancing them is critical for de-raining methods. We propose an end-to-end network, called dual-task de-raining network (DTDN), consisting of two sub-networks: generative adversarial network (GAN) and convolutional neural network (CNN), to remove rain streaks via coordinating the two mutually exclusive objectives self-adaptively. DTDN-GAN is mainly used to remove structural rain streaks, and DTDN-CNN is designed to recover details in original images. We also design a training algorithm to train these two sub-networks of DTDN alternatively, which share same weights but use different training sets. We further enrich two existing datasets to approximate the distribution of real rain streaks. Experimental results show that our method outperforms several recent state-of-the-art methods, based on both benchmark testing datasets and real rainy images.

preprint2020arXiv

Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda

Participation on social media platforms has many benefits but also poses substantial threats. Users often face an unintended loss of privacy, are bombarded with mis-/disinformation, or are trapped in filter bubbles due to over-personalized content. These threats are further exacerbated by the rise of hidden AI-driven algorithms working behind the scenes to shape users' thoughts, attitudes, and behavior. We investigate how multimedia researchers can help tackle these problems to level the playing field for social media users. We perform a comprehensive survey of algorithmic threats on social media and use it as a lens to set a challenging but important research agenda for effective and real-time user nudging. We further implement a conceptual prototype and evaluate it with experts to supplement our research agenda. This paper calls for solutions that combat the algorithmic threats on social media by utilizing machine learning and multimedia content analysis techniques but in a transparent manner and for the benefit of the users.

preprint2020arXiv

Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework

Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making automatic video summarization more realistic by addressing them. Firstly, the currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking dataset VISIOCITY which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and can be used for other vision problems. Secondly, for long videos, human reference summaries are difficult to obtain. We present a novel recipe based on pareto optimality to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea. We propose a simple recipe VISIOCITY-SUM to enhance an existing model using a combination of losses and demonstrate

preprint2020arXiv

ByeGlassesGAN: Identity Preserving Eyeglasses Removal for Face Images

In this paper, we propose a novel image-to-image GAN framework for eyeglasses removal, called ByeGlassesGAN, which is used to automatically detect the position of eyeglasses and then remove them from face images. Our ByeGlassesGAN consists of an encoder, a face decoder, and a segmentation decoder. The encoder is responsible for extracting information from the source face image, and the face decoder utilizes this information to generate glasses-removed images. The segmentation decoder is included to predict the segmentation mask of eyeglasses and completed face region. The feature vectors generated by the segmentation decoder are shared with the face decoder, which facilitates better reconstruction results. Our experiments show that ByeGlassesGAN can provide visually appealing results in the eyeglasses-removed face images even for semi-transparent color eyeglasses or glasses with glare. Furthermore, we demonstrate significant improvement in face recognition accuracy for face images with glasses by applying our method as a pre-processing step in our face recognition experiment.

preprint2020arXiv

Rate distortion optimization over large scale video corpus with machine learning

We present an efficient codec-agnostic method for bitrate allocation over a large scale video corpus with the goal of minimizing the average bitrate subject to constraints on average and minimum quality. Our method clusters the videos in the corpus such that videos within one cluster have similar rate-distortion (R-D) characteristics. We train a support vector machine classifier to predict the R-D cluster of a video using simple video complexity features that are computationally easy to obtain. The model allows us to classify a large sample of the corpus in order to estimate the distribution of the number of videos in each of the clusters. We use this distribution to find the optimal encoder operating point for each R-D cluster. Experiments with AV1 encoder show that our method can achieve the same average quality over the corpus with $22\%$ less average bitrate.

preprint2020arXiv

Semantics Preserving Hierarchy based Retrieval of Indian heritage monuments

Monument classification can be performed on the basis of their appearance and shape from coarse to fine categories. Although there is much semantic information present in the monuments which is reflected in the eras they were built, its type or purpose, the dynasty which established it, etc. Particularly, Indian subcontinent exhibits a huge deal of variation in terms of architectural styles owing to its rich cultural heritage. In this paper, we propose a framework that utilizes hierarchy to preserve semantic information while performing image classification or image retrieval. We encode the learnt deep embeddings to construct a dictionary of images and then utilize a re-ranking framework on the the retrieved results using DeLF features. The semantic information preserved in these embeddings helps to classify unknown monuments at higher level of granularity in hierarchy. We have curated a large, novel Indian heritage monuments dataset comprising of images of historical, cultural and religious importance with subtypes of eras, dynasties and architectural styles. We demonstrate the performance of the proposed framework in image classification and retrieval tasks and compare it with ot

preprint2020arXiv

JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures

JPEG has been a widely used lossy image compression codec for nearly three decades. The JPEG standard allows to use customized quantization table; however, it's still a challenging problem to find an optimal quantization table within acceptable computational cost. This work tries to solve the dilemma of balancing between computational cost and image specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the simulated annealing technique is applied to texture mosaic images to search for an optimal quantization table for each texture category. We use pre-trained VGG-16 CNN model to learn those texture features and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image specific optimal quantization table. On the Kodak dataset with the quality setting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEG standard table with a slightly 0.35% FSIM decrease, which is visually unperceivable. The proposed JQF method achieves per image optimality for JPEG encoding with less than one second additional timing cost. The onlin

preprint2020arXiv

Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos

Smartphone is the most successful consumer electronic product in today's mobile social network era. The smartphone camera quality and its image post-processing capability is the dominant factor that impacts consumer's buying decision. However, the quality evaluation of photos taken from smartphones remains a labor-intensive work and relies on professional photographers and experts. As an extension of the prior CNN-based NR-IQA approach, we propose a multi-task deep CNN model with scene type detection as an auxiliary task. With the shared model parameters in the convolution layer, the learned feature maps could become more scene-relevant and enhance the performance. The evaluation result shows improved SROCC performance compared to traditional NR-IQA methods and single task CNN-based models.

preprint2020arXiv

Personal Food Model

Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related

preprint2020arXiv

Quality of Service (QoS): Measurements of Video Streaming

Nowadays video streaming is growing over the social clouds, where end-users always want to share High Definition (HD) videos among friends. Mostly videos were recorded via smartphones and other HD devices and short time videos have a big file size. The big file size of videos required high bandwidth to upload and download on the Internet and also required more time to load in a web page for play. So avoiding this problem social cloud compress videos during the upload for smooth play and fast loading in a web page. Compression decreases the video quality which also decreases the quality of experience of end users. In this paper we measure the QoS of different standard video file formats on social clouds; they varied from each other in resolution, audio/video bitrate, and storage size.

preprint2020arXiv

High Efficiency Rate Control for Versatile Video Coding Based on Composite Cauchy Distribution

In this work, we propose a novel rate control algorithm for Versatile Video Coding (VVC) standard based on its distinct rate-distortion characteristics. By modelling the transform coefficients with the composite Cauchy distribution, higher accuracy compared with traditional distributions has been achieved. Based on the transform coefficient modelling, the theoretically derived R-Q and D-Q models which have been shown to deliver higher accuracy in characterizing RD characteristics for sequences with different content are incorporated into the rate control process. Furthermore, to establish an adaptive bit allocation scheme, the dependency between different levels of frames is modelled by a dependency factor to describe relationship between the reference and to-be-coded frames. Given the derived R-Q and D-Q relationships, as well as the dependency factor, an adaptive bit allocation scheme is developed for optimal bits allocation. We implement the proposed algorithm on VVC Test Model (VTM) 3.0. Experiments show that due to proper bit allocation, for low delay configuration the proposed algorithm can achieve 1.03% BD-Rate saving compared with the default rate control algorithm and 2.96

preprint2020arXiv

Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors

Reflections often degrade the quality of the image by obstructing the background scene. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised-learning models. However, these models require an extensive number of image pairs to perform well, especially on natural images with reflection, which is difficult to achieve in practice. In this paper, we propose a novel unsupervised framework for single-image reflection separation. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional networks on a target image to generate two exclusive background and reflection layers. In particular, we design a new architecture of the network to embed semantic features extracted from a pre-trained deep classification network, which gives more meaningful separation similar to human perception. Quantitative and qualitative results on commonly used datasets in the literature show that our method's performance is at least on par with the state-of-the-art supervised methods and, occasionally, be

preprint2020arXiv

Dual Attention GANs for Semantic Image Synthesis

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrat

preprint2020arXiv

Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset for Personality Assessment

Automatically detecting personality traits can aid several applications, such as mental health recognition and human resource management. Most datasets introduced for personality detection so far have analyzed these traits for each individual in isolation. However, personality is intimately linked to our social behavior. Furthermore, surprisingly little research has focused on personality analysis using low resource languages. To this end, we present a novel peer-to-peer Hindi conversation dataset- Vyaktitv. It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation. The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants. We release the dataset for public use, as well as perform preliminary statistical analysis along the different dimensions. Finally, we also discuss various other applications and tasks for which the dataset can be employed.