Reviewerly/exHarmony huggingface dataset:
Robust Video Portrait Reenactment via Personalized Representation Quantization

While progress has been made in the field of portrait reenactment, the problem of how to produce high-fidelity and robust videos remains. Recent studies normally find it challenging to handle rarely seen target poses due to the limitation of source data. This paper proposes the Video Portrait via Non-local Quantization Modeling (VPNQ) framework, which produces pose- and disturbance-robust reenactable video portraits. Our key insight is to learn position-invariant quantized local patch representations and build a mapping between simple driving signals and local textures with non-local spatial-temporal modeling. Specifically, instead of learning a universal quantized codebook, we identify that a personalized one can be trained to preserve desired position-invariant local details better. Then, a simple representation of projected landmarks can be used as sufficient driving signals to avoid 3D rendering. Following, we employ a carefully designed Spatio-Temporal Transformer to predict reasonable and temporally consistent quantized tokens from the driving signal. The predicted codes can be decoded back to robust and high-quality videos. Comprehensive experiments have been conducted to validate the effectiveness of our approach.
Sentiment Analysis of Social Media Platform Reviews Using the Naïve Bayes Classifier Algorithm

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.
Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment

As deep learning models become popular, there is a lot of need for deploying them to diverse device environments. Because it is costly to develop and optimize a neural network for every single environment, there is a line of research to search neural networks for multiple target environments efficiently. However, existing works for such a situation still suffer from requiring many GPUs and expensive costs. Motivated by this, we propose a novel neural network optimization framework named Bespoke for low-cost deployment. Our framework searches for a lightweight model by replacing parts of an original model with randomly selected alternatives, each of which comes from a pretrained neural network or the original model. In the practical sense, Bespoke has two significant merits. One is that it requires near zero cost for designing the search space of neural networks. The other merit is that it exploits the sub-networks of public pretrained neural networks, so the total cost is minimal compared to the existing works. We conduct experiments exploring Bespoke's the merits, and the results show that it finds efficient models for multiple targets with meager cost.
Multi-Level Compositional Reasoning for Interactive Instruction Following

Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks, e.g., bring a cup of coffee. To address the challenge, we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction. We call it Multi-level Compositional Reasoning Agent (MCR-Agent). Specifically, we learn a three-level action policy. At the highest level, we infer a sequence of human-interpretable subgoals to be executed based on language instructions by a high-level policy composition controller. At the middle level, we discriminatively control the agent’s navigation by a master policy by alternating between a navigation policy and various independent interaction policies. Finally, at the lowest level, we infer manipulation actions with the corresponding object masks using the appropriate interaction policy. Our approach not only generates human interpretable subgoals but also achieves 2.03% absolute gain to comparable state of the arts in the efficiency metric (PLWSR in unseen set) without using rule-based planning or a semantic spatial memory. The code is available at https://github.com/yonseivnl/mcr-agent.
DocEdit: Language-Guided Document Editing

Professional document editing tools require a certain level of expertise to perform complex edit operations. To make editing tools accessible to increasingly novice users, we investigate intelligent document assistant systems that can make or suggest edits based on a user's natural language request. Such a system should be able to understand the user's ambiguous requests and contextualize them to the visual cues and textual content found in a document image to edit localized unstructured text and structured layouts. To this end, we propose a new task of language-guided localized document editing, where the user provides a document and an open vocabulary editing request, and the intelligent system produces a command that can be used to automate edits in real-world document editing software. In support of this task, we curate the DocEdit dataset, a collection of approximately 28K instances of user edit requests over PDF and design templates along with their corresponding ground truth software executable commands. To our knowledge, this is the first dataset that provides a diverse mix of edit operations with direct and indirect references to the embedded text and visual objects such as paragraphs, lists, tables, etc. We also propose DocEditor, a Transformer-based localization-aware multimodal (textual, spatial, and visual) model that performs the new task. The model attends to both document objects and related text contents which may be referred to in a user edit request, generating a multimodal embedding that is used to predict an edit command and associated bounding box localizing it. Our proposed model empirically outperforms other baseline deep learning approaches by 15-18%, providing a strong starting point for future work.
MIDMs: Matching Interleaved Diffusion Models for Exemplar-Based Image Translation

We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-based matching-then-generation framework. However, in this framework, matching errors induced by the difficulty of semantic matching across cross-domain, e.g., sketch and photo, can be easily propagated to the generation step, which in turn leads to the degenerated results. Motivated by the recent success of diffusion models, overcoming the shortcomings of GANs, we incorporate the diffusion models to overcome these limitations. Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image. In addition, to improve the reliability of diffusion process, we design confidence-aware process using cycle-consistency to consider only confident regions during translation. Experimental results show that our MIDMs generate more plausible images than state-of-the-art methods.
FastAMI – a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics

Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data. However, as datasets grow, comparing clusterings with an adjustment for chance becomes computationally difficult, preventing unbiased ground-truth comparisons and solution selection. We propose FastAMI, a Monte Carlo-based method to efficiently approximate the Adjusted Mutual Information (AMI) and extend it to the Standardized Mutual Information (SMI). The approach is compared with the exact calculation and a recently developed variant of the AMI based on pairwise permutations, using both synthetic and real data. In contrast to the exact calculation our method is fast enough to enable these adjusted information-theoretic comparisons for large datasets while maintaining considerably more accurate results than the pairwise approach.
Counterfactual Dynamics Forecasting – a New Setting of Quantitative Reasoning

Rethinking and introspection are important elements of human intelligence. To mimic these capabilities, counterfactual reasoning has attracted attention of AI researchers recently, which aims to forecast the alternative outcomes for hypothetical scenarios (“what-if”). However, most existing approaches focused on qualitative reasoning (e.g., casual-effect relationship). It lacks a well-defined description of the differences between counterfactuals and facts, as well as how these differences evolve over time. This paper defines a new problem formulation - counterfactual dynamics forecasting - which is described in middle-level abstraction under the structural causal models (SCM) framework and derived as ordinary differential equations (ODEs) as low-level quantitative computation. Based on it, we propose a method to infer counterfactual dynamics considering the factual dynamics as demonstration. Moreover, the evolution of differences between facts and counterfactuals are modelled by an explicit temporal component. The experimental results on two dynamical systems demonstrate the effectiveness of the proposed method.
Understanding the Generalization Performance of Spectral Clustering Algorithms

The theoretical analysis of spectral clustering is mainly devoted to consistency, while there is little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: relaxed RatioCut and relaxed NCut. Our analysis follows the two practical steps of spectral clustering algorithms: continuous solution and discrete solution. Firstly, we provide the convergence rate of the excess risk bounds between the empirical continuous optimal solution and the population-level continuous optimal solution. Secondly, we show the fundamental quantity influencing the excess risk between the empirical discrete optimal solution and the population-level discrete optimal solution. At the empirical level, algorithms can be designed to reduce this quantity. Based on our theoretical analysis, we propose two novel algorithms that can penalize this quantity and, additionally, can cluster the out-of-sample data without re-eigendecomposition on the overall samples. Numerical experiments on toy and real datasets confirm the effectiveness of our proposed algorithms.
Explicit Invariant Feature Induced Cross-Domain Crowd Counting

Cross-domain crowd counting has shown progressively improved performance. However, most methods fail to explicitly consider the transferability of different features between source and target domains. In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. The main idea is to explicitly extract domain-invariant features from both source and target domains, which builds a bridge to transfer more rich knowledge between two domains. The framework consists of three parts, global feature decoupling (GFD), relation exploration and alignment (REA), and graph-guided knowledge enhancement (GKE). In the GFD module, domain-invariant features are efficiently decoupled from domain-specific ones in two domains, which allows the model to distinguish crowds features from backgrounds in the complex scenes. In the REA module both inter-domain relation graph (Inter-RG) and intra-domain relation graph (Intra-RG) are built. Specifically, Inter-RG aggregates multi-scale domain-invariant features between two domains and further aligns local-level invariant features. Intra-RG preserves taskrelated specific information to assist the domain alignment. Furthermore, GKE strategy models the confidence of pseudolabels to further enhance the adaptability of the target domain. Various experiments show our method achieves state-of-theart performance on the standard benchmarks. Code is available at https://github.com/caiyiqing/IF-CKT.
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs

Simulations that produce three-dimensional data are ubiquitous in science, ranging from fluid flows to plasma physics. We propose a similarity model based on entropy, which allows for the creation of physically meaningful ground truth distances for the similarity assessment of scalar and vectorial data, produced from transport and motion-based simulations. Utilizing two data acquisition methods derived from this model, we create collections of fields from numerical PDE solvers and existing simulation data repositories. Furthermore, a multiscale CNN architecture that computes a volumetric similarity metric (VolSiM) is proposed. To the best of our knowledge this is the first learning method inherently designed to address the challenges arising for the similarity assessment of high-dimensional simulation data. Additionally, the tradeoff between a large batch size and an accurate correlation computation for correlation-based loss functions is investigated, and the metric's invariance with respect to rotation and scale operations is analyzed. Finally, the robustness and generalization of VolSiM is evaluated on a large range of test data, as well as a particularly challenging turbulence case study, that is close to potential real-world applications.
Deconstructed Generation-Based Zero-Shot Model

Recent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods. However, current literature has overlooked the fundamental principles of these methods and has made limited progress in a complex manner. In this paper, we aim to deconstruct the generator-classifier framework and provide guidance for its improvement and extension. We begin by breaking down the generator-learned unseen class distribution into class-level and instance-level distributions. Through our analysis of the role of these two types of distributions in solving the GZSL problem, we generalize the focus of the generation-based approach, emphasizing the importance of (i) attribute generalization in generator learning and (ii) independent classifier learning with partially biased data. We present a simple method based on this analysis that outperforms SotAs on four public GZSL datasets, demonstrating the validity of our deconstruction. Furthermore, our proposed method remains effective even without a generative model, representing a step towards simplifying the generator-classifier structure. Our code is available at https://github.com/cdb342/DGZ.
Analysis of Information Technology Services Using the ITIL V.3 Framework

Information technology services have been widely used in various agencies. This is done because information technology services help many business processes in an agency. Government agencies, companies, and education are examples of agencies that utilize information technology services to support their business processes. One educational institution that uses IT services to support its business processes is the UKDW library. The UKDW Library utilizes IT services for borrowing, returning, searching for books and journals, checking for plagiarism, and more. To further improve the quality of IT services, evaluating and developing them is necessary to better respond to business needs. In this study, researchers measured the maturity level of an IT service in the UKDW library. Researchers use the ITIL V3 framework in measuring maturity levels. This research focuses on the service operation domain and its five sub-domains (problem management, event management, incident management, access management, and request fulfillment). The final results of the study show that IT services in the UKDW library are at the managed level, which means that information technology services in the UKDW library have been planned and carried out routinely. There are standards for documentation, and process performance measurement has been implemented.
Unifying Vision-Language Representation Space with Single-Tower Transformer

Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this work, we explore the hypothesis that an image and caption can be regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a one-tower model for vision-language pretraining (VLP), and propose One Representation (OneR) as a simple yet effective framework for our goal. We discover intriguing properties that distinguish OneR from the previous works that have modality-specific representation spaces such as zero-shot localization, text-guided visual reasoning and multi-modal retrieval, and present analyses to provide insights into this new form of multi-modal representation learning. Thorough evaluations demonstrate the potential of a unified modality-agnostic VLP framework.
Pose-Guided 3D Human Generation in Indoor Scene

In this work, we address the problem of scene-aware 3D human avatar generation based on human-scene interactions. In particular, we pay attention to the fact that physical contact between a 3D human and a scene (i.e., physical human-scene interactions) requires a geometrical alignment to generate natural 3D human avatar. Motivated by this fact, we present a new 3D human generation framework that considers geometric alignment on potential contact areas between 3D human avatars and their surroundings. In addition, we introduce a compact yet effective human pose classifier that classifies the human pose and provides potential contact areas of the 3D human avatar. It allows us to adaptively use geometric alignment loss according to the classified human pose. Compared to state-of-the-art method, our method can generate physically and semantically plausible 3D humans that interact naturally with 3D scenes without additional post-processing. In our evaluations, we achieve the improvements with more plausible interactions and more variety of poses than prior research in qualitative and quantitative analysis. Project page: https://bupyeonghealer.github.io/phin/.
A Formal Metareasoning Model of Concurrent Planning and Execution

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. When timing is tight, there may be insufficient time to complete the search for a plan before it is time to act. By commencing execution before search concludes, one gains time to search by making planning and execution concurrent. However, this incurs the risk of making incorrect action choices, especially if actions are irreversible. This tradeoff between opportunity and risk is the problem addressed in this paper. Our main contribution is to formally define this setting as an abstract metareasoning problem. We find that the abstract problem is intractable. However, we identify special cases that are solvable in polynomial time, develop greedy solution algorithms, and, through tests on instances derived from search problems, find several methods that achieve promising practical performance. This work lays the foundation for a principled time-aware executive that concurrently plans and executes.
VASR: Visual Analogies of Situation Recognition

A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/
Leveraging Weighted Cross-Graph Attention for Visual and Semantic Enhanced Video Captioning Network

Video captioning has become a broad and interesting research area. Attention-based encoder-decoder methods are extensively used for caption generation. However, these methods mostly utilize the visual attentive feature to highlight the video regions while overlooked the semantic features of the available captions. These semantic features contain significant information that helps to generate highly informative human description-like captions. Therefore, we propose a novel visual and semantic enhanced video captioning network, named as VSVCap, that efficiently utilizes multiple ground truth captions. We aim to generate captions that are visually and semantically enhanced by exploiting both video and text modalities. To achieve this, we propose a fine-grained cross-graph attention mechanism that captures detailed graph embedding correspondence between visual graphs and textual knowledge graphs. We have performed node-level matching and structure-level reasoning between the weighted regional graph and knowledge graph. The proposed network achieves promising results on three benchmark datasets, i.e., YouTube2Text, MSR-VTT, and VATEX. The experimental results show that our network accurately captures all key objects, relationships, and semantically enhanced events of a video to generate human annotation-like captions.