by jsendak | Mar 7, 2024 | Computer Science
arXiv:2403.03740v1 Announce Type: cross
Abstract: In the domain of image layout representation learning, the critical process of translating image layouts into succinct vector forms is increasingly significant across diverse applications, such as image retrieval, manipulation, and generation. Most approaches in this area heavily rely on costly labeled datasets and notably lack in adapting their modeling and learning methods to the specific nuances of photographic image layouts. This shortfall makes the learning process for photographic image layouts suboptimal. In our research, we directly address these challenges. We innovate by defining basic layout primitives that encapsulate various levels of layout information and by mapping these, along with their interconnections, onto a heterogeneous graph structure. This graph is meticulously engineered to capture the intricate layout information within the pixel domain explicitly. Advancing further, we introduce novel pretext tasks coupled with customized loss functions, strategically designed for effective self-supervised learning of these layout graphs. Building on this foundation, we develop an autoencoder-based network architecture skilled in compressing these heterogeneous layout graphs into precise, dimensionally-reduced layout representations. Additionally, we introduce the LODB dataset, which features a broader range of layout categories and richer semantics, serving as a comprehensive benchmark for evaluating the effectiveness of layout representation learning methods. Our extensive experimentation on this dataset demonstrates the superior performance of our approach in the realm of photographic image layout representation learning.
Emerging Trends in Photographic Image Layout Representation Learning
Image layout representation learning is an important area in multimedia information systems. The ability to translate image layouts into vector forms is crucial for various applications, such as image retrieval, manipulation, and generation. However, existing approaches in this field often rely on labeled datasets, which can be expensive and limit the adaptability of the models.
In this research, the authors tackle these challenges by introducing innovative techniques in photographic image layout representation learning. They define basic layout primitives that capture different levels of layout information and map them onto a heterogeneous graph structure. This graph is designed to explicitly capture the intricate layout information within the pixel domain.
Furthermore, the authors propose novel pretext tasks and customized loss functions for self-supervised learning of these layout graphs. This approach allows their network architecture to effectively compress the heterogeneous layout graphs into precise, dimensionally-reduced layout representations.
To evaluate the effectiveness of their approach, the authors introduce the LODB dataset. This dataset includes a broader range of layout categories and richer semantics, serving as a comprehensive benchmark for layout representation learning methods.
The experimentation conducted on the LODB dataset demonstrates the superior performance of the proposed approach in the domain of photographic image layout representation learning.
Multidisciplinary Nature
This research encompasses multiple disciplines, combining aspects of computer vision, machine learning, and data representation. The authors leverage techniques from these fields to address the challenges in photographic image layout representation learning.
By incorporating graph theory, the authors create a heterogeneous graph structure that captures the complex relationships and layout information within the pixel domain. This multidisciplinary approach allows for a more accurate representation of image layouts and enables better performance in downstream tasks.
Relationship to Multimedia Information Systems
Multimedia information systems deal with the handling, processing, and retrieval of different types of media, including images. Image layout representation learning plays a vital role in these systems by providing an efficient way to organize and represent visual information.
The techniques proposed in this research can enhance multimedia information systems by enabling more precise image retrieval and manipulation. The dimensionally-reduced layout representations obtained through the proposed network architecture can facilitate faster and more accurate matching of user queries with relevant images.
Related to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts explored in this research have implications for animations, artificial reality, augmented reality, and virtual realities.
Animations rely heavily on image layout representation to create visually appealing sequences. By improving the representation learning process for photographic image layouts, this research can contribute to more realistic and engaging animations.
Artificial reality, augmented reality, and virtual realities heavily rely on accurate representation of visual scenes. The innovations in layout representation learning introduced in this research can enhance the realism and quality of these immersive experiences.
Overall, this research opens up new possibilities for improving the representation and understanding of photographic image layouts through a multi-disciplinary approach. The proposed techniques and benchmark dataset pave the way for further advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 7, 2024 | Computer Science
Expert Commentary: Self-Supervised Learning in Biosignals
Self-supervised learning has proven to be a powerful approach in the domains of audio, vision, and speech, where large labeled datasets are often available. However, in the field of biosignal analysis, such as electroencephalography (EEG), labeled data is scarce, making self-supervised learning even more relevant and necessary.
In this work, the authors propose a self-supervised model specifically designed for EEG signals. They introduce a state space-based deep learning architecture that demonstrates robust performance and remarkable parameter efficiency. This is crucial in biosignal analysis, where computational resources are often limited.
Adapting Self-Supervised Learning to Biosignal Analysis
One of the key challenges in applying self-supervised learning to biosignals is the domain difference between multimedia modalities and biosignals. The traditional objectives and techniques used in self-supervised learning may not be directly applicable in the context of EEG signals. Therefore, the innovation in this work lies in adapting self-supervised learning methods to account for the idiosyncrasies of EEG signals.
The authors propose a novel knowledge-guided pre-training objective that specifically addresses the unique characteristics of EEG signals. This objective aims to capture the underlying structure and dynamics of EEG data, enabling the model to learn meaningful representations that can improve downstream performance on various inference tasks.
Improved Embedding Representation Learning and Downstream Performance
The results of this study demonstrate the effectiveness of the proposed self-supervised model for EEG. The model provides improved embedding representation learning, indicating that it can capture more relevant and discriminative information from the EEG signals. This is of great importance as accurate representation learning is crucial for subsequent analysis and classification tasks.
In addition to improved representation learning, the proposed self-supervised model also shows superior downstream performance compared to prior works on exemplary tasks. This suggests that the learned representations are of high quality and can be effectively utilized for various biosignal analysis tasks, such as seizure detection, sleep stage classification, or brain-computer interface applications.
Data Efficiency and Reduced Pre-training Data Requirement
Another significant advantage of the proposed self-supervised model is its parameter efficiency and reduced pre-training data requirement. By leveraging the knowledge-guided pre-training objective, the authors were able to achieve performance equivalent to prior works with significantly less pre-training data. This is particularly valuable in the context of limited labeled data availability in biosignal analysis, as it allows for more efficient and quicker model training.
In conclusion, this work demonstrates the potential of self-supervised learning in biosignal analysis, specifically focusing on EEG signals. By adapting self-supervised learning methods and introducing a knowledge-guided pre-training objective, the authors have achieved improved representation learning, downstream performance, and parameter efficiency. These findings open up new possibilities for leveraging large-scale unlabelled data to enhance the performance of biosignal inference tasks.
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by jsendak | Mar 6, 2024 | Computer Science
arXiv:2403.02693v1 Announce Type: new
Abstract: Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user’s viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to introduce minimal transmission and computation overhead for mobile terminals. We also propose a model-agnostic meta-learning (MAML) based saliency prediction network trainer, which provides a few-sample fast training solution to obtain the prediction model by utilizing the information from the past models. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results show that our prediction approach can work in real-time for live video streaming and can achieve higher accuracies compared to other existing prediction methods on mobile end, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. We observe the accuracy of MFVP is 8.1$%$ to 28.7$%$ higher than other algorithms and achieves 3.73$%$ to 14.96$%$ higher average quality level and 49.6$%$ to 74.97$%$ less quality level change than other algorithms.
Expert Commentary: Advanced Viewport Prediction for Adaptive 360-Degree Video Streaming
Viewport prediction is a critical task in adaptive 360-degree video streaming, as it helps determine the user’s viewing area within a frame, enabling bitrate control algorithms to allocate resources efficiently. Traditionally, various methods have been used for viewport prediction, ranging from less accurate statistical tools to highly precise deep neural networks. However, implementing complex deep learning methods on mobile devices with limited computational capabilities has been a challenge.
This research proposes an advanced learning-based viewport prediction approach that specifically addresses the limitations of mobile terminals. By carefully designing the approach, the authors aim to minimize transmission and computation overhead while still achieving accurate viewport prediction. One of the key contributions of this work is the introduction of a model-agnostic meta-learning (MAML) based saliency prediction network trainer, which enables fast training with few samples and utilizes past model information.
The authors also discuss the integration of this mobile-friendly viewport prediction approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. By combining their prediction approach with a bitrate adaptation algorithm, the researchers aim to significantly improve the streaming quality of experience (QoE).
The multidisciplinary nature of this work is evident in its convergence of concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Adaptive video streaming is a key aspect of multimedia information systems, and viewport prediction plays a crucial role in enhancing user immersion and interaction in animations, artificial reality, augmented reality, and virtual reality applications.
The experiment results provided in the research paper demonstrate the effectiveness of the proposed approach. The mobile-friendly viewport prediction (MFVP) approach achieves higher accuracies compared to other existing prediction methods on mobile devices. Additionally, when combined with the bitrate adaptation algorithm, it leads to higher average quality levels and reduces quality level changes during streaming. These improvements contribute to an enhanced streaming QoE for users.
In conclusion, this research presents an advanced learning-based viewport prediction approach that specifically addresses the challenges of implementing deep learning methods on mobile devices. By integrating this approach into a 360-degree video live streaming system and combining it with a bitrate adaptation algorithm, the researchers successfully improve the streaming QoE. This work highlights the multidisciplinary nature of multimedia information systems and its connections to animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 6, 2024 | Computer Science
Data similarity has always played a crucial role in understanding the convergence behavior of federated learning methods. However, relying solely on data similarity assumptions can be problematic, as it often requires fine-tuning step sizes based on the level of data similarity. This can lead to slow convergence speeds for federated methods when data similarity is low.
In this paper, the authors introduce a novel and unified framework for analyzing the convergence of federated learning algorithms that eliminates the need for data similarity conditions. Their analysis focuses on an inequality that captures the impact of step sizes on algorithmic convergence performance.
By applying their theorems to well-known federated algorithms, the authors derive precise expressions for three commonly used step size schedules: fixed, diminishing, and step-decay step sizes. These expressions are independent of data similarity conditions, providing a significant advantage over traditional approaches.
To validate their approach, the authors conduct comprehensive evaluations of the performance of these federated learning algorithms on benchmark datasets with varying levels of data similarity. The results show significant improvements in convergence speed and overall performance, marking a significant advancement in federated learning research.
This research is highly relevant and timely, as federated learning continues to gain traction in various domains where data privacy and distributed data sources are a concern. The ability to analyze convergence without relying on data similarity assumptions opens up new possibilities for applying federated learning to a wider range of scenarios.
From a practical standpoint, these findings have important implications for practitioners and researchers working with federated learning algorithms. The ability to use fixed, diminishing, or step-decay step sizes without the need for data similarity fine-tuning can save significant time and effort in training models.
Moreover, the improved convergence speed and overall performance demonstrated by the proposed step size strategies are likely to have a positive impact on the scalability and practicality of federated learning. With faster convergence, federated learning becomes a more viable option for real-time and resource-constrained systems.
That being said, further research is still needed to explore the potential limitations and generalizability of the proposed framework. It would be interesting to investigate the performance of the derived step size schedules on more complex deep neural network architectures and different types of datasets.
Additionally, as federated learning continues to evolve, it would be valuable to examine how the proposed framework interacts with other advancements in the field, such as adaptive step size strategies or communication-efficient algorithms.
In conclusion, this paper presents a significant contribution to the field of federated learning by introducing a novel framework for analyzing convergence without data similarity assumptions. The derived step size schedules offer improved convergence speed and overall performance, paving the way for wider adoption of federated learning in practical applications.
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by jsendak | Mar 4, 2024 | Computer Science
arXiv:2403.00752v1 Announce Type: new
Abstract: Low-latency video streaming over 5G has become rapidly popular over the last few years due to its increased usage in hosting virtual events, online education, webinars, and all-hands meetings. Our work aims to address the absence of studies that reveal the real-world behavior of low-latency video streaming. To that end, we provide an experimental methodology and measurements, collected in a US metropolitan area over a commercial 5G network, that correlates application-level QoE and lower-layer metrics on the devices, such as RSRP, RSRQ, handover records, etc., under both static and mobility scenarios. We find that RAN-side information, which is readily available on every cellular device, has the potential to enhance throughput estimation modules of video streaming clients, ultimately making low-latency streaming more resilient against network perturbations and handover events.
Analysis of Low-Latency Video Streaming over 5G
In recent years, low-latency video streaming over 5G has seen a significant increase in popularity. This is mainly due to its widespread usage in various domains such as virtual events, online education, webinars, and all-hands meetings. However, despite its growing prevalence, there is a lack of studies that provide a detailed understanding of the real-world behavior of low-latency video streaming.
This is where the work presented in this article comes into play. The authors aim to address this gap by providing an experimental methodology and measurements that shed light on the relationship between application-level Quality of Experience (QoE) and lower-layer metrics on devices. These lower-layer metrics include factors such as RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), and handover records.
The experiments conducted in a US metropolitan area over a commercial 5G network encompass both static and mobility scenarios. This diversity in testing conditions helps to capture the different challenges that can arise during low-latency video streaming. By correlating the application-level QoE with the lower-layer metrics, the authors are able to provide valuable insights into the impact of network perturbations and handover events on the streaming experience.
One noteworthy finding of this research is the potential of RAN-side (Radio Access Network) information in enhancing throughput estimation modules of video streaming clients. By leveraging the readily available RAN-side information on cellular devices, the authors suggest that it is possible to improve the resilience of low-latency streaming against network perturbations and handover events. This has significant implications for the quality and reliability of low-latency video streaming, ensuring a seamless experience for users even in dynamic network environments.
Multi-Disciplinary Nature
What makes this research particularly interesting is its multi-disciplinary nature. It combines concepts from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Low-latency video streaming is a fundamental component of these domains, as it enables real-time interactions and immersive experiences for users.
The findings of this study not only contribute to the field of low-latency video streaming but also have broader implications for multimedia information systems. By understanding the impact of lower-layer metrics on application-level QoE, researchers and practitioners can develop more effective algorithms and protocols for multimedia content delivery. This leads to improvements in user satisfaction, engagement, and overall experience.
Furthermore, the insights gained from this research can be applied to other areas such as animations, artificial reality, augmented reality, and virtual realities. These technologies heavily rely on low-latency streaming to provide seamless and interactive experiences to users. By optimizing the streaming process based on the correlation between application-level QoE and lower-layer metrics, these technologies can deliver more realistic and immersive content.
Future Directions
This research opens up several avenues for future exploration. Firstly, further studies can be conducted in different geographical locations to assess the generalizability of the findings. Different network infrastructures, user behaviors, and environmental factors may impact the performance of low-latency video streaming. By broadening the scope of the research, a more comprehensive understanding of the real-world behavior of low-latency streaming can be achieved.
In addition, future work could focus on the development of machine learning and AI-based models that leverage the RAN-side information to enhance the performance of video streaming clients. By using predictive algorithms, these models can proactively adapt to network perturbations and handover events, ensuring a smooth streaming experience for users.
Moreover, as multimedia technologies continue to evolve, the integration of low-latency streaming with emerging concepts such as virtual reality and augmented reality becomes crucial. Future research could explore the optimization of low-latency streaming for these immersive technologies, considering factors specific to 3D environments, real-time interactions, and spatial audio.
In conclusion, this study provides valuable insights into the real-world behavior of low-latency video streaming over 5G networks. By correlating application-level QoE with lower-layer metrics, the authors highlight the potential of RAN-side information in improving the resilience of streaming clients. The multi-disciplinary nature of this research makes it relevant not only to low-latency streaming but also to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 4, 2024 | Computer Science
Expert Commentary: User Feedback-Based Counterfactual Explanation (UFCE)
Machine learning models have become integral to various real-world applications, including decision-making processes in areas like finance, healthcare, and autonomous systems. However, the complexity of these models often leads to a lack of transparency and interpretability, making it difficult for users to understand the underlying rationale behind the decisions made by the models. This is where explainable artificial intelligence (XAI) and counterfactual explanations (CEs) come into play.
Counterfactual explanations provide users with understandable insights into how to achieve a desired outcome by suggesting minimal modifications to initial inputs. They help bridge the gap between the inherently complex black-box nature of machine learning models and the need for human-understandable explanations. However, current CE algorithms have their limitations, which the novel methodology introduced in this study aims to overcome.
The User Feedback-Based Counterfactual Explanation (UFCE) methodology enables the inclusion of user constraints, allowing users to express their preferences and limitations. By doing so, UFCE focuses on finding the smallest modifications within actionable features, rather than operating within the entire feature space. This approach not only enhances the interpretability of the explanations but also ensures that the suggested changes are practical and feasible.
One of the important aspects addressed by UFCE is the consideration of feature dependence. Machine learning models often rely on the relationships and interactions between different features to make accurate predictions. By taking these dependencies into account, UFCE enables more accurate identification of the key contributors to the outcome, providing users with more useful and actionable explanations.
The study conducted three experiments using five datasets to evaluate the performance of UFCE compared to two well-known CE methods. The evaluation metrics used were proximity, sparsity, and feasibility. The results demonstrated that UFCE outperformed the existing methods in these aspects, indicating its effectiveness in generating superior counterfactual explanations.
Furthermore, the study highlighted the impact of user constraints on the generation of feasible CEs. By allowing users to impose their preferences and limitations, UFCE takes into account the practicality of the suggested modifications. This ensures that the explanations provided are not only theoretically valid but also actionable in real-world scenarios.
The introduction of UFCE as a novel methodology in the field of explainable artificial intelligence holds great promise for improving the transparency and interpretability of machine learning models. By incorporating user feedback and constraints, UFCE goes beyond mere explanation and empowers users to actively participate in the decision-making process. This approach has significant implications for fields such as healthcare, where trust and understanding in AI systems are critical for adoption and acceptance.
Key Takeaways:
- Counterfactual explanations (CEs) provide insights into achieving desired outcomes with minimal modifications to inputs.
- Current CE algorithms often overlook key contributors and disregard practicality.
- User Feedback-Based Counterfactual Explanation (UFCE) addresses these limitations.
- UFCE allows the inclusion of user constraints and considers feature dependence.
- UFCE outperforms existing CE methods in terms of proximity, sparsity, and feasibility.
- User constraints influence the generation of feasible CEs.
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