by jsendak | Feb 8, 2024 | Computer Science
Abstract:
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system. The data silos by the above Distribution Gap could result in a significant performance decline in multi-agent perception.
In this paper, we thoroughly examine the impact of the distribution gap on existing multi-agent perception systems. To break the data silos, we introduce the Feature Distribution-aware Aggregation (FDA) framework for cross-domain learning to mitigate the above Distribution Gap in multi-agent perception.
FDA comprises two key components: Learnable Feature Compensation Module and Distribution-aware Statistical Consistency Module, both aimed at enhancing intermediate features to minimize the distribution gap among multi-agent features.
Intensive experiments on the public OPV2V and V2XSet datasets underscore FDA’s effectiveness in point cloud-based 3D object detection, presenting it as an invaluable augmentation to existing multi-agent perception systems.
Expert Commentary:
The problem of data silos and the distribution gap in multi-agent perception systems is a significant challenge in the field. This paper brings attention to this issue and proposes an innovative solution called the Feature Distribution-aware Aggregation (FDA) framework.
The FDA framework is designed to address the distribution gap by introducing two key components: the Learnable Feature Compensation Module and the Distribution-aware Statistical Consistency Module. These components aim to enhance intermediate features and minimize the distribution gap among multi-agent features.
This approach is particularly valuable in scenarios where diverse agents from different companies are involved in a multi-agent perception system. Even if these agents use identical neural network architectures for feature extraction, the private and independent data sources for training each agent can result in significant performance decline due to data silos.
The Learnable Feature Compensation Module and the Distribution-aware Statistical Consistency Module help break down these data silos by enhancing the intermediate features and ensuring consistency among the features extracted by different agents. By minimizing the distribution gap, the FDA framework enables better cooperation and coordination among the agents in a multi-agent perception system.
The effectiveness of the FDA framework is supported by intensive experiments on public datasets such as OPV2V and V2XSet. The positive results obtained in point cloud-based 3D object detection highlight the value of FDA as an augmentation to existing multi-agent perception systems.
In conclusion, the paper highlights the importance of addressing the distribution gap and data silos in multi-agent perception systems. The proposed FDA framework provides a promising solution to mitigate these issues and improve the overall performance of such systems. Further research and implementation of FDA in real-world scenarios are warranted to explore its full potential.
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by jsendak | Feb 7, 2024 | Computer Science
Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment algorithms for general purposes are surveyed and concluded according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performances of the state-of-the-art video quality assessment measures are compared and analyzed. This survey provides a systematic overview of both classical works and recent progresses in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.
Expert Commentary: Video Quality Assessment in the Era of Multimedia Information Systems
Video quality assessment is a crucial area within the field of multimedia information systems, which encompasses various aspects of video processing and delivery. As mentioned in the article, the increasing demand for video content and the growth of internet communication highlight the need for accurate and rapid assessment of video quality. This is particularly important due to the presence of quality degradations that occur during different stages of video signal acquisition, compression, transmission, and display.
One noteworthy aspect of video quality assessment is its multidisciplinary nature. It encompasses concepts from diverse fields such as video processing, human perception, signal processing, and data analysis. By integrating knowledge from these disciplines, researchers have conducted both subjective and objective video quality assessment studies over the past two decades.
Subjective Video Quality Assessment
The first aspect explored in this survey is subjective video quality assessment methodologies and databases. Subjective assessment involves human observers who rate the quality of videos based on their visual experience. This approach is essential for validating the performance of objective video quality metrics. Several databases have been created, containing videos with various perceptual characteristics and degradation types. These databases serve as valuable resources for evaluating video quality algorithms.
Objective Video Quality Assessment
The next focus of the survey is on objective video quality assessment algorithms for general purposes. Objective assessment aims to develop computational models that can predict perceived video quality without the need for human judgments. These algorithms utilize different methodologies such as machine learning, statistical analysis, and mathematical models to estimate video quality. The survey provides an overview of these algorithms, allowing researchers to understand their strengths and limitations.
Video Quality Assessment for Specific Applications
As video applications evolve, it becomes crucial to develop objective quality assessment measures tailored to specific contexts. This survey covers objective video quality assessment measures for emerging topics such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), and audio-visual videos. Each of these applications poses unique challenges, and the survey highlights the state-of-the-art measures employed in these domains.
Analyzing State-of-the-Art Measures
Finally, the survey compares and analyzes the performances of state-of-the-art video quality assessment measures. This analysis helps researchers gauge the effectiveness of different algorithms and identify areas for improvement. By understanding the strengths and weaknesses of existing measures, researchers can strive to develop more accurate and robust video quality assessment techniques.
In summary, this comprehensive survey provides a systematic overview of video quality assessment in the field of multimedia information systems. It covers subjective and objective assessment methodologies, explores specific applications, and compares the performances of state-of-the-art measures. This valuable resource enables researchers to access the field quickly and conduct relevant research, thus contributing to the advancement of video quality assessment in various domains like animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Feb 7, 2024 | Computer Science
The Singularity Problem in Convection-Diffusion Models: A New Approach
In this article, we delve into the analysis and numerical results of a singular perturbed convection-diffusion problem and its discretization. Specifically, we focus on the scenario where the convection term dominates the problem, leading to interesting challenges in accurately approximating the solution.
Optimal Norm and Saddle Point Reformulation
One of the key contributions of our research is the introduction of the concept of optimal norm and saddle point reformulation in the context of mixed finite element methods. By utilizing these concepts, we were able to derive new error estimates specifically tailored for cases where the convection term is dominant.
These new error estimates provide valuable insights into the behavior of the numerical approximation and help us understand the limitations of traditional approaches. By comparing these estimates with those obtained from the standard linear Galerkin discretization, we gain a deeper understanding of the non-physical oscillations observed in the discrete solutions.
Saddle Point Least Square Discretization
In exploring alternative discretization techniques, we propose a novel approach called the saddle point least square discretization. This method utilizes quadratic test functions, which offers a more accurate representation of the solution compared to the linear Galerkin discretization.
Through our analysis, we shed light on the non-physical oscillations observed in the discrete solutions obtained using this method. Understanding the reasons behind these oscillations allows us to refine the discretization scheme and improve the accuracy of the numerical solution.
Relating Different Discretization Methods
In addition to our own proposed method, we also draw connections between other existing discretization methods commonly used for convection-diffusion problems. We emphasize the upwinding Petrov Galerkin method and the stream-line diffusion discretization method, highlighting their resulting linear systems and comparing the error norms associated with each.
By examining these relationships, we gain insights into the strengths and weaknesses of each method and can make informed decisions regarding their suitability for different scenarios. This comparative analysis allows us to choose the most efficient approximation technique for more general singular perturbed problems, including those with convection domination in multidimensional settings.
In conclusion, our research provides a comprehensive analysis of singular perturbed convection-diffusion problems, with a specific focus on cases dominated by the convection term. By introducing new error estimates, proposing a novel discretization method, and relating different approaches, we offer valuable insights into the numerical approximation of these problems. Our findings can be extended to tackle more complex and multidimensional scenarios, advancing the field of numerical approximation for singular perturbed problems.
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by jsendak | Feb 6, 2024 | Computer Science
Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness. However, the minimal inter-class variation in similar action sequences often leads to confusion. The inherent spatiotemporal coupling characteristics make it challenging to mine the subtle differences in joint motion trajectories, which is critical for distinguishing confusing fine-grained actions. To alleviate this problem, we propose a Wavelet-Attention Decoupling (WAD) module that utilizes discrete wavelet transform to effectively disentangle salient and subtle motion features in the time-frequency domain. Then, the decoupling attention adaptively recalibrates their temporal responses. To further amplify the discrepancies in these subtle motion features, we propose a Fine-grained Contrastive Enhancement (FCE) module to enhance attention towards trajectory features by contrastive learning. Extensive experiments are conducted on the coarse-grained dataset NTU RGB+D and the fine-grained dataset FineGYM. Our methods perform competitively compared to state-of-the-art methods and can discriminate confusing fine-grained actions well.
Succinctness and Robustness in Skeleton-based Action Recognition
Skeleton-based action recognition has gained significant attention in the field of multimedia information systems due to its potential for achieving succinct and robust results. This approach involves analyzing the motion trajectories of human skeleton joints to classify different actions. However, a major challenge in this area is the minimal inter-class variation in similar action sequences, which often leads to confusion.
The Challenge of Mining Subtle Differences
The spatiotemporal coupling characteristics inherent in skeleton-based action recognition make it difficult to mine the subtle differences in joint motion trajectories. These subtle differences are crucial for accurately distinguishing fine-grained actions that are otherwise confusingly similar. To address this challenge, the proposed Wavelet-Attention Decoupling (WAD) module utilizes discrete wavelet transform to effectively disentangle salient and subtle motion features in the time-frequency domain.
Recalibrating Temporal Responses with Decoupling Attention
The WAD module is further enhanced with decoupling attention, which adaptively recalibrates the temporal responses of disentangled motion features. This adaptive recalibration helps amplify the discrepancies between subtle motion features, making it easier to discriminate fine-grained actions. The utilization of wavelet transform and decoupling attention reflects the multi-disciplinary nature of this approach, combining concepts from signal processing and neural network architectures.
Enhancing Attention with Fine-grained Contrastive Learning
To further enhance the attention towards trajectory features, the proposed Fine-grained Contrastive Enhancement (FCE) module employs contrastive learning techniques. This module amplifies the discrepancies in subtle motion features through a comparative analysis, enabling better discrimination of fine-grained actions. This integration of contrastive learning methods demonstrates the interdisciplinarity of multimedia information systems with machine learning and computer vision techniques.
Evaluating the Proposed Methods
To evaluate the effectiveness of the proposed methods, extensive experiments are conducted on two datasets: the coarse-grained dataset NTU RGB+D and the fine-grained dataset FineGYM. The results show that the proposed methods perform competitively compared to state-of-the-art methods in skeleton-based action recognition. The ability to discriminate confusing fine-grained actions well highlights the potential for these methods to improve various applications, such as video surveillance, motion analysis, and human-computer interaction.
In conclusion, this article presents a novel approach to address the challenges of skeleton-based action recognition. By incorporating wavelet transform, decoupling attention, and contrastive learning techniques, this approach offers enhanced discrimination capabilities for fine-grained actions. The integration of concepts from signal processing, neural networks, and machine learning showcases the multi-disciplinary nature of multimedia information systems. Future research may focus on exploring the application of these methods in other domains, such as virtual reality and augmented reality, where accurate recognition of human actions is crucial for immersive experiences.
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by jsendak | Feb 6, 2024 | Computer Science
Expert Commentary: The Potential of ChatGPT in Education
As the integration of artificial intelligence tools, such as ChatGPT, in the education system becomes more prevalent, it is crucial to examine students’ perceptions and suggestions for incorporating such tools into specific courses. This experience report sheds light on the potential of ChatGPT in a computer science course, offering valuable insights into its impact on learning experience and programming skills.
Enhancing Learning Experience and Personalized Learning
Students participating in a ChatGPT activity, involving code completion and analysis, reported several benefits of using the tool. One significant advantage highlighted by the participants was ChatGPT’s ability to provide immediate responses to their queries. This instant feedback can be highly valuable in a computer science course, enabling students to progress at their own pace and address any uncertainties without delay.
Moreover, the participants emphasized that ChatGPT supports personalized learning. By tailoring responses based on individual queries, it assists students in acquiring the specific knowledge and understanding they require. This personalized approach promotes a sense of ownership over the learning process and empowers students to explore their unique interests and challenges.
The Need for Balancing Reliance on AI Tools
While acknowledging the advantages of using ChatGPT, the participants expressed concerns about its potential impact on their critical thinking and problem-solving skills. They raised valid points regarding the risk of over-reliance, highlighting the need to strike a careful balance between utilizing ChatGPT as a support tool and cultivating essential cognitive abilities.
It is important for educators and curriculum designers to consider these concerns when integrating AI tools like ChatGPT into computer science courses. The goal should be to create an environment that encourages students to engage in independent thinking, creativity, and complex problem-solving, while still utilizing the benefits of AI for assistance and reinforcement.
Implications for Educators, Curriculum Designers, and Policymakers
The findings of this research carry significant implications for various stakeholders in the education domain. Educators can use these insights to shape their approach to integrating AI tools in the classroom effectively. By balancing the use of intelligent tools like ChatGPT with other educational activities, teachers can optimize learning outcomes and ensure the development of students’ critical thinking abilities.
Curriculum designers can benefit from these findings by specifically considering the incorporation of AI tools into computer science curricula. They can develop guidelines and strategies that encourage the responsible use of AI while cultivating essential skills that align with educational goals.
Policymakers also need to be aware of the potential impact of AI tools in education. They can use the findings to inform policies that provide guidelines for educators and institutions, ensuring responsible integration and best practices in AI adoption.
This research contributes to the ongoing dialogue about the integration of AI tools, like ChatGPT, in educational contexts. The valuable insights gained from student perspectives lay the groundwork for future exploration and refinement of AI integration in computer science courses and beyond.
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by jsendak | Feb 5, 2024 | Computer Science
Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame by frame, requiring low latency and being highly sensitive to network fluctuations. In this paper, we model the quality of experience (QoE) for real-time XR video transmission on a frame-by-frame basis. Based on the proposed QoE model, we formulate an optimization problem that maximizes QoE with constraints on wireless resources and long-term energy consumption. We utilize Lyapunov optimization to transform the original problem into a single-frame optimization problem and then allocate wireless subchannels. We propose an adaptive XR video bitrate algorithm that employs a Long Short Term Memory (LSTM) based Deep Q-Network (DQN) algorithm for video bitrate selection. Through numerical results, we show that our proposed algorithm outperforms the baseline algorithms, with the average QoE improvements of 5.9% to 80.0%.
Analysis and Expert Insights
This article highlights the significance of Extended Reality (XR) services in the context of 5G and future 6G networks. XR encompasses a wide range of technologies including Virtual Reality (VR), Augmented Reality (AR), and Artificial Reality (AR) that provide immersive and interactive experiences to users. As XR video transmission is highly sensitive to network fluctuations and requires low latency, ensuring a high Quality of Experience (QoE) becomes crucial.
The paper introduces a QoE model for real-time XR video transmission on a frame-by-frame basis. By modeling the QoE, the authors aim to optimize wireless resources and long-term energy consumption while maximizing user satisfaction. They employ Lyapunov optimization techniques to transform the problem into a single-frame optimization problem, allowing for efficient allocation of wireless subchannels.
To further enhance the performance of XR video transmission, the authors propose an adaptive XR video bitrate algorithm that utilizes a Long Short Term Memory (LSTM) based Deep Q-Network (DQN). This algorithm dynamically selects the video bitrate based on the current network conditions, ensuring optimal video quality and reducing the impact of network fluctuations on user experience.
The results of their numerical experiments demonstrate the superiority of their proposed algorithm over baseline algorithms. The average QoE improvements ranging from 5.9% to 80.0% indicate the effectiveness of their approach in enhancing user satisfaction during real-time XR video transmission.
Overall, this research contributes to the wider field of multimedia information systems by addressing the unique challenges posed by real-time XR video transmission in 5G and future 6G networks. The multi-disciplinary nature of the concepts discussed, including wireless communication, optimization theory, deep learning, and human-computer interaction, showcases the complexity of developing advanced XR services. By leveraging cutting-edge techniques such as Lyapunov optimization and LSTM-based DQN, this paper provides valuable insights into improving QoE and optimizing resource allocation in XR video transmission.
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