OpenRaiser/Intern-Atlas huggingface dataset:
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Test-time adaptation (TTA) adapts the pre-trained models during inference using unlabeled test data and has received a lot of research attention due to its potential practical value. Unfortunately, without any label supervision, existing TTA methods rely heavily on heuristic or empirical studies. Where to update the model always falls into suboptimal or brings more computational resource consumption. Meanwhile, there is still a significant performance gap between the TTA approaches and their supervised counterparts. Motivated by active learning, in this work, we propose the active test-time adaptation for semantic segmentation setup. Specifically, we introduce the human-in-the-loop pattern during the testing phase, which queries very few labels to facilitate predictions and model updates in an online manner. To do so, we propose a simple but effective ATASeg framework, which consists of two parts, i.e., model adapter and label annotator. Extensive experiments demonstrate that ATASeg bridges the performance gap between TTA methods and their supervised counterparts with only extremely few annotations, even one click for labeling surpasses known SOTA TTA methods by 2.6% average mIoU on ACDC benchmark. Empirical results imply that progress in either the model adapter or the label annotator will bring improvements to the ATASeg framework, giving it large research and reality potential.
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning
Venue: arXiv cs.RO 2023
Year: 2023

Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing intelligent drill boom control methods rely on a hierarchical control framework based on inverse kinematics. However, these methods are generally time-consuming due to the computational complexity of inverse kinematics and the inefficiency of the sequential execution of multiple joints. To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL). We develop an integrated drill boom control framework that utilizes a parameterized policy to directly generate control inputs for all joints at each time step, taking advantage of joint posture and target hole information. By formulating the hole-seeking task as a Markov decision process, contemporary mainstream RL algorithms can be directly employed to learn a hole-seeking policy, thus eliminating the need for inverse kinematics solutions and promoting cooperative multi-joint control. To enhance the drilling accuracy throughout the entire drilling process, we devise a state representation that combines Denavit-Hartenberg joint information and preview hole-seeking discrepancy data. Simulation results show that the proposed method significantly outperforms traditional methods in terms of hole-seeking accuracy and time efficiency.
VividTalk: One-Shot Audio-Driven Talking Head Generation Based on 3D Hybrid Prior
Venue: arXiv cs.CV 2023
Year: 2023

Audio-driven talking head generation has drawn much attention in recent years, and many efforts have been made in lip-sync, expressive facial expressions, natural head pose generation, and high video quality. However, no model has yet led or tied on all these metrics due to the one-to-many mapping between audio and motion. In this paper, we propose VividTalk, a two-stage generic framework that supports generating high-visual quality talking head videos with all the above properties. Specifically, in the first stage, we map the audio to mesh by learning two motions, including non-rigid expression motion and rigid head motion. For expression motion, both blendshape and vertex are adopted as the intermediate representation to maximize the representation ability of the model. For natural head motion, a novel learnable head pose codebook with a two-phase training mechanism is proposed. In the second stage, we proposed a dual branch motion-vae and a generator to transform the meshes into dense motion and synthesize high-quality video frame-by-frame. Extensive experiments show that the proposed VividTalk can generate high-visual quality talking head videos with lip-sync and realistic enhanced by a large margin, and outperforms previous state-of-the-art works in objective and subjective comparisons.
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods try to overcome this issue, but need samples from the target domain. However, this might not always be feasible for various reasons and therefore domain generalization methods are useful as they do not require any target data. We present a new diffusion-based domain extension (DIDEX) method and employ a diffusion model to generate a pseudo-target domain with diverse text prompts. In contrast to existing methods, this allows to control the style and content of the generated images and to introduce a high diversity. In a second step, we train a generalizing model by adapting towards this pseudo-target domain. We outperform previous approaches by a large margin across various datasets and architectures without using any real data. For the generalization from GTA5, we improve state-of-the-art mIoU performance by 3.8% absolute on average and for SYNTHIA by 11.8% absolute, marking a big step for the generalization performance on these benchmarks. Code is available at https://github.com/JNiemeijer/DIDEX
Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets
Venue: arXiv cs.RO 2023
Year: 2023

Huge image data sets are the fundament for the development of the perception of automated driving systems. A large number of images is necessary to train robust neural networks that can cope with diverse situations. A sufficiently large data set contains challenging situations and objects. For testing the resulting functions, it is necessary that these situations and objects can be found and extracted from the data set. While it is relatively easy to record a large amount of unlabeled data, it is far more difficult to find demanding situations and objects. However, during the development of perception systems, it must be possible to access challenging data without having to perform lengthy and time-consuming annotations. A developer must therefore be able to search dynamically for specific situations and objects in a data set. Thus, we designed a method which is based on state-of-the-art neural networks to search for objects with certain properties within an image. For the ease of use, the query of this search is described using natural language. To determine the time savings and performance gains, we evaluated our method qualitatively and quantitatively on automotive data sets.
TCP Slice: A semi-distributed TCP algorithm for Delay-constrained Applications
Venue: arXiv cs.NI 2023
Year: 2023

The TCP congestion control protocol serves as the cornerstone of reliable internet communication. However, as new applications require more specific guarantees regarding data rate and delay, network management must adapt. Thus, service providers are shifting from decentralized to centralized control of the network using a software-defined network controller (SDN). The SDN classifies applications and allocates logically separate resources called slices, over the physical network. We propose TCP Slice, a congestion control algorithm that meets specific delay and bandwidth guarantees. Obtaining closed-form delay bounds for a client is challenging due to dependencies on other clients and their traffic stochasticity. We use network calculus to derive the client's delay bound and incorporate it as a constraint in the Network Utility Maximization problem. We solve the resulting optimization using dual decomposition and obtain a semi-distributed TCP protocol that can be implemented with the help of SDN controller and the use of an Explicit Congestion Notification (ECN) bit. Additionally, we also propose a proactive approach for congestion control using digital twin. TCP Slice represents a significant step towards accommodating evolving internet traffic patterns and the need for better network management in the face of increasing application diversity.
Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection
Venue: arXiv cs.LG 2023
Year: 2023

Understanding electric vehicle (EV) charging on the distribution network is key to effective EV charging management and aiding decarbonization across the energy and transport sectors. Advanced metering infrastructure has allowed distribution system operators and utility companies to collect high-resolution load data from their networks. These advancements enable the non-intrusive load monitoring (NILM) technique to detect EV charging using load measurement data. While existing studies primarily focused on NILM for EV charging detection in individual households, there is a research gap on EV charging detection at the feeder level, presenting unique challenges due to the combined load measurement from multiple households. In this paper, we develop a novel and effective approach for EV detection at the feeder level, involving sliding-window feature extraction and classical machine learning techniques, specifically models like XGBoost and Random Forest. Our developed method offers a lightweight and efficient solution, capable of quick training. Moreover, our developed method is versatile, supporting both offline and online EV charging detection. Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98.88% in offline detection and 93.01% in online detection.
DFTWS for blockchain: Deterministic, Fair and Transparent Winner Selection
Venue: arXiv cs.CR 2023
Year: 2023

This publication describes the block winner selection process that will be used in a novel Proof-of-Useful-Work blockchain for High Energy Physics that the authors are currently working on. Instead of spamming hashing operations to mine blocks, miners will be running Monte Carlo simulations to support a real-world HEP experiment with useful data. The block problems will be defined by a Root Authority which is represented by a HEP experiment like CBM. The focus in this publication is a mechanism that allows the Root Authority to select a winner from a list of nodes that solved a block problem. The mechanism is designed so that winner selection is deterministic, fair and transparent. This mechanism allows every node to verify the fairness of the winner selection process without giving the nodes a tool to be able to improve their own winning chances.
Implicit Learning of Scene Geometry from Poses for Global Localization
Venue: arXiv cs.CV 2023
Year: 2023

Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this paper, we propose to utilize these minimal available labels (.i.e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations (\textit{X, Y, Z coordinates}) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.
Trust, distrust, and appropriate reliance in (X)AI: a survey of empirical evaluation of user trust
Venue: arXiv cs.HC 2023
Year: 2023

A current concern in the field of Artificial Intelligence (AI) is to ensure the trustworthiness of AI systems. The development of explainability methods is one prominent way to address this, which has often resulted in the assumption that the use of explainability will lead to an increase in the trust of users and wider society. However, the dynamics between explainability and trust are not well established and empirical investigations of their relation remain mixed or inconclusive. In this paper we provide a detailed description of the concepts of user trust and distrust in AI and their relation to appropriate reliance. For that we draw from the fields of machine learning, human-computer interaction, and the social sciences. Furthermore, we have created a survey of existing empirical studies that investigate the effects of AI systems and XAI methods on user (dis)trust. With clarifying the concepts and summarizing the empirical investigations, we aim to provide researchers, who examine user trust in AI, with an improved starting point for developing user studies to measure and evaluate the user's attitude towards and reliance on AI systems.
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases
Venue: arXiv cs.LG 2023
Year: 2023

Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data mining or machine learning tasks conducted on relational databases. However, it is worth noting that there are limited machine learning models specifically designed for relational databases, as most models are primarily tailored for single table settings. Consequently, the prevalent approach for training machine learning models on data stored in relational databases involves performing feature engineering to merge the data from multiple tables into a single table and subsequently applying single table models. This approach not only requires significant effort in feature engineering but also destroys the inherent relational structure present in the data. To address these challenges, we propose a novel framework called Graph-based Feature Synthesis (GFS). GFS formulates the relational database as a heterogeneous graph, thereby preserving the relational structure within the data. By leveraging the inductive bias from single table models, GFS effectively captures the intricate relationships inherent in each table. Additionally, the whole framework eliminates the need for manual feature engineering. In the extensive experiment over four real-world multi-table relational databases, GFS outperforms previous methods designed for relational databases, demonstrating its superior performance.
Right-sizing compute resource allocations for bioinformatics tools with Total Perspective Vortex
Venue: arXiv cs.DC 2023
Year: 2023

In biomedical research, computational methods have become indispensable and their use is increasing, making the efficient allocation of computing resources paramount. Practitioners routinely allocate resources far in excess of what is required for batch processing jobs, leading to not just inflated wait times and costs, but also unnecessary carbon emissions. This is not without reason however, as accurately determining resource needs is complex, affected by the nature of tools, data size, and analysis parameters, especially on popular servers that handle numerous jobs. The Galaxy platform, a web-based hub for biomedical analysis used globally by scientists, exemplifies this challenge. Serving nearly half a million registered users and managing around 2 million monthly jobs, Galaxy's growth outpaces the resources at its disposal. This is necessitating smarter resource utilization. To address this, we have developed a tool named Total Perspective Vortex (TPV) - a software package that right-sizes resource allocations for each job. TPV is able to dynamically set resource requirements for individual jobs and perform meta-scheduling across heterogeneous resources. It also includes a first-ever community-curated database of default resource requirements for nearly 1,000 popular bioinformatics tools. Deployments in Galaxy Australia and Europe demonstrate its effectiveness with meta-scheduling user jobs and an improved experience for systems administrators managing Galaxy servers.
Evaluating the Claims of "SAT Requires Exhaustive Search"
Venue: arXiv cs.CC 2023
Year: 2023

In this paper, we take a closer look at the claims made by Xu and Zhou in their paper "SAT Requires Exhaustive Search" [XZ23], which claims to provide a lower bound on the complexity of the so-called Model RB. Xu and Zhou conclude that their result implies a separation between P and NP, since the lower bound purportedly proves that the Strong Exponential Time Hypothesis (SETH) is true. In examining Xu and Zhou's arguments, we find a flaw in their main theorems. The authors assume that an algorithm for Model RB must have a certain structure that can leverage downward self-reducibility, and argue that such an algorithm cannot run in polynomial time. We argue that this structure is not guaranteed to exist and thus their paper neither proves SETH to be true nor proves P $\neq$ NP.
Can we truly transfer an actor's genuine happiness to avatars? An investigation into virtual, real, posed and spontaneous faces
Venue: arXiv cs.CV 2023
Year: 2023

A look is worth a thousand words is a popular phrase. And why is a simple look enough to portray our feelings about something or someone? Behind this question are the theoretical foundations of the field of psychology regarding social cognition and the studies of psychologist Paul Ekman. Facial expressions, as a form of non-verbal communication, are the primary way to transmit emotions between human beings. The set of movements and expressions of facial muscles that convey some emotional state of the individual to their observers are targets of studies in many areas. Our research aims to evaluate Ekman's action units in datasets of real human faces, posed and spontaneous, and virtual human faces resulting from transferring real faces into Computer Graphics faces. In addition, we also conducted a case study with specific movie characters, such as SheHulk and Genius. We intend to find differences and similarities in facial expressions between real and CG datasets, posed and spontaneous faces, and also to consider the actors' genders in the videos. This investigation can help several areas of knowledge, whether using real or virtual human beings, in education, health, entertainment, games, security, and even legal matters. Our results indicate that AU intensities are greater for posed than spontaneous datasets, regardless of gender. Furthermore, there is a smoothing of intensity up to 80 percent for AU6 and 45 percent for AU12 when a real face is transformed into CG.
Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction
Venue: arXiv cs.IT 2023
Year: 2023

In the realm of 5G communication systems, the accuracy of Channel State Information (CSI) prediction is vital for optimizing performance. This letter introduces a pioneering approach: the Spectral-Temporal Graph Neural Network (STEM GNN), which fuses spatial relationships and temporal dynamics of the wireless channel using the Graph Fourier Transform. We compare the STEM GNN approach with conventional Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for CSI prediction. Our findings reveal a significant enhancement in overall communication system performance through STEM GNNs. For instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns often overlooked by traditional models, offering improvements in beamforming, interference mitigation, and ultra-reliable low-latency communication (URLLC).
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model
Venue: arXiv cs.CV 2023
Year: 2023

Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limited localized shape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self-supervised fashion. The resulting TailorMe model enables shape sampling, localized shape manipulation, and fast inference from given surface scans.
How Generative-AI can be Effectively used in Government Chatbots
Venue: arXiv cs.CL 2023
Year: 2023

With the rapid development of artificial intelligence and breakthroughs in machine learning and natural language processing, intelligent question-answering robots have become widely used in government affairs. This paper conducts a horizontal comparison between Guangdong Province's government chatbots, ChatGPT, and Wenxin Ernie, two large language models, to analyze the strengths and weaknesses of existing government chatbots and AIGC technology. The study finds significant differences between government chatbots and large language models. China's government chatbots are still in an exploratory stage and have a gap to close to achieve "intelligence." To explore the future direction of government chatbots more deeply, this research proposes targeted optimization paths to help generative AI be effectively applied in government chatbot conversations.