by jsendak | Oct 19, 2024 | Computer Science
arXiv:2410.12813v1 Announce Type: new
Abstract: Video Temporal Grounding (VTG) aims to ground specific segments within an untrimmed video corresponding to the given natural language query. Existing VTG methods largely depend on supervised learning and extensive annotated data, which is labor-intensive and prone to human biases. To address these challenges, we present ChatVTG, a novel approach that utilizes Video Dialogue Large Language Models (LLMs) for zero-shot video temporal grounding. Our ChatVTG leverages Video Dialogue LLMs to generate multi-granularity segment captions and matches these captions with the given query for coarse temporal grounding, circumventing the need for paired annotation data. Furthermore, to obtain more precise temporal grounding results, we employ moment refinement for fine-grained caption proposals. Extensive experiments on three mainstream VTG datasets, including Charades-STA, ActivityNet-Captions, and TACoS, demonstrate the effectiveness of ChatVTG. Our ChatVTG surpasses the performance of current zero-shot methods.
Expert Commentary: Video Temporal Grounding with ChatVTG
The field of video temporal grounding has been revolutionized by the emergence of ChatVTG, a novel approach that utilizes Video Dialogue Large Language Models (LLMs) for zero-shot video temporal grounding. This approach addresses the challenges of supervised learning and the need for extensive annotated data, which are both labor-intensive and prone to human biases.
Multi-disciplinary Nature of ChatVTG
ChatVTG combines concepts from various disciplines, including natural language processing, computer vision, and machine learning. By leveraging Video Dialogue LLMs, ChatVTG generates multi-granularity segment captions for videos, enabling coarse temporal grounding without requiring paired annotation data. This multi-disciplinary approach allows for a more robust and accurate temporal grounding process.
Relation to Multimedia Information Systems
ChatVTG is closely related to the field of multimedia information systems, as it addresses the problem of effectively retrieving specific segments within untrimmed videos based on natural language queries. By utilizing Video Dialogue LLMs, ChatVTG enhances the ability of multimedia information systems to process and understand video content, making it easier for users to find relevant information within videos.
Animations, Artificial Reality, Augmented Reality, and Virtual Realities
While ChatVTG does not directly focus on these specific fields, its application in multimedia information systems and video temporal grounding can have implications for animations, artificial reality, augmented reality, and virtual realities. As these technologies continue to evolve, the need for accurate and efficient temporal grounding in videos becomes crucial for creating immersive and interactive experiences. ChatVTG’s ability to precisely ground segments within videos can contribute to the development of more realistic animations, lifelike artificial realities, enhanced augmented reality experiences, and immersive virtual realities.
Promising Results and Future Possibilities
Extensive experiments on popular VTG datasets have demonstrated the effectiveness of ChatVTG, surpassing the performance of current zero-shot methods. The success of ChatVTG opens up new possibilities for further advancements in video temporal grounding and the larger field of multimedia information systems. Future research might explore the integration of ChatVTG with other computer vision techniques, such as object detection and scene understanding, to enhance the accuracy and granularity of temporal grounding. Additionally, the exploration of unsupervised or weakly supervised learning approaches could further reduce the reliance on annotated data and expand the applicability of ChatVTG to a wider range of video datasets.
Conclusion
ChatVTG represents a significant advancement in the field of video temporal grounding, offering a zero-shot approach that leverages Video Dialogue LLMs for more accurate and efficient segment retrieval. Its multi-disciplinary nature, relation to multimedia information systems, and potential impact on animations, artificial reality, augmented reality, and virtual realities make ChatVTG a promising innovation. As research continues to progress, ChatVTG is expected to catalyze further developments in the field, ultimately leading to enhanced multimedia experiences and improved video content retrieval.
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by jsendak | Oct 19, 2024 | Computer Science
Expert Commentary:
The Current Landscape of Generative AI Integration in Healthcare
The rapid advancement of generative AI technologies, such as large language models (LLMs), has brought about unprecedented opportunities and challenges for healthcare institutions. In this study, the authors present a comprehensive environmental scan of the generative AI infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States.
The findings reveal that most healthcare organizations are still in the experimental phase of GenAI deployment. This suggests that while there is recognition of the potential benefits, there is also a cautious approach to implementation, likely due to the complexity and ethical considerations associated with these technologies.
Governance Models and Ethical Considerations
One of the key highlights of this study is the significant variations in governance models across institutions. While there is a strong preference for centralized decision-making, there are notable gaps in workforce training and ethical oversight. This indicates a need for a more coordinated approach to GenAI governance, with collaboration among senior leaders, clinicians, information technology staff, and researchers. Effective governance is essential to ensure that GenAI technologies are implemented ethically and with transparency.
The study also raises concerns regarding GenAI bias, data security, and stakeholder trust. These concerns align with broader discussions in the field of AI ethics and emphasize the importance of addressing these issues to build trust in the use of GenAI in healthcare. Bias in AI algorithms can lead to disparities in care and exacerbate existing inequalities in healthcare delivery. Therefore, it is crucial for institutions to actively work on addressing and mitigating bias in GenAI systems to ensure fairness and equity.
Opportunities and Roadmap for GenAI Integration
This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare. Healthcare institutions can use these findings as a roadmap for leveraging GenAI for improved quality of care and operational efficiency. As organizations move beyond the experimental phase, they can focus on developing standardized protocols for GenAI integration and establishing clear governance frameworks that address workforce training, ethical considerations, and stakeholder engagement. Collaboration between clinical experts, AI researchers, and IT professionals will be crucial in achieving these goals.
In conclusion, the rapid advancement of GenAI technologies presents both exciting opportunities and complex challenges for healthcare institutions. This study provides a comprehensive analysis of the current status of GenAI integration and highlights the need for coordinated governance models, transparency, and ethical considerations. By addressing these issues, healthcare organizations can harness the full potential of GenAI to improve patient outcomes and transform healthcare delivery.
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by jsendak | Oct 17, 2024 | Computer Science
arXiv:2410.12220v1 Announce Type: new
Abstract: For decades, the Bj{o}ntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4-5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? This paper addresses these concerns by reevaluating BD estimation. We present a novel approach employing a parameterized deep neural network to model R-D curves with high precision across various metrics, accompanied by a comprehensive R-D dataset. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results.
The Importance of Bjøntegaard Delta (BD) Estimation in Codec Evaluation
In the field of multimedia information systems, codec evaluation is crucial to assess the performance of different compression algorithms. One widely used metric for evaluating codec Rate-Distortion (R-D) performance is the Bjøntegaard Delta (BD). BD is a measure of the bitrate savings achieved by a codec compared to a reference codec, while maintaining the same level of distortion.
Traditionally, BD has been determined using a small number of R-D data points, typically around 4-5. However, recent advancements in codecs and quality metrics raise questions about the reliability of such estimation. Are the improvements in performance of new codecs and tools genuine, or are they simply artifacts of estimation flaws?
This paper investigates this issue by proposing a novel approach to BD estimation. The authors employ a parameterized deep neural network to model R-D curves with high precision across various metrics. They also provide a comprehensive R-D dataset for evaluation.
This approach allows for a more accurate and reliable estimation of BD. By employing a deep neural network, the researchers are able to model the complex relationship between bitrate, distortion, and other quality metrics. This not only provides a better assessment of the reliability of BD calculations but also serves as a precise BD estimator.
The multi-disciplinary nature of this study is evident in its integration of concepts from different fields, including multimedia information systems, artificial intelligence, and statistics. The use of deep neural networks to model R-D curves showcases the application of machine learning in multimedia research.
Furthermore, this research has implications for the wider field of multimedia information systems. Accurate BD estimation is crucial for comparing and evaluating different codecs, which is essential for the development and optimization of compression algorithms. By advocating for the adoption of rigorous R-D sampling and reliability metrics, this study ensures the validity and reliability of future compression research.
In addition, the findings of this research are relevant to the study of animations, artificial reality, augmented reality, and virtual reality. These technologies heavily rely on multimedia information systems and compression algorithms. The accurate estimation of BD enables better optimization of multimedia content for these applications, leading to improved user experiences.
In conclusion, this paper presents a significant contribution to the field of codec evaluation by reevaluating BD estimation. The proposed approach using a parameterized deep neural network provides a more accurate and reliable estimation of BD. This research serves as a reminder of the importance of rigorous evaluation and highlights the multi-disciplinary nature of multimedia information systems.
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by jsendak | Oct 17, 2024 | Computer Science
Analysis: LSFS – Revolutionizing File Management with LLM-based Semantic Approach
The traditional file system paradigm has long posed challenges to users and developers alike with its complex navigation and reliance on precise commands. However, with the emergence of large language models (LLMs), there is immense potential for improving file management systems through natural language prompts and semantic approaches. This article discusses the LSFS (LLM-based Semantic File System) as a groundbreaking solution to enhance file management with an intuitive and intelligent framework.
The Limitations of Conventional File Systems
Traditional file systems have long relied on manual navigation through complex folder hierarchies and cryptic file names. This not only poses a bottleneck to usability but also requires users to have a deep understanding of command syntax and folder structures. These limitations can impede productivity and frustrate users who are not well-versed in technical aspects.
Introducing LSFS: Leveraging LLMs for Semantic File Management
LSFS proposes a revolutionary solution that incorporates LLMs to enable users or agents to interact with files through natural language prompts. By leveraging the power of LLMs, LSFS facilitates semantic file management, eliminating the need for precise commands and complex navigation. This approach simplifies file operations and empowers users with a more intuitive and efficient file management experience.
Comprehensive API Set & Semantic Indexing
At the macro-level, LSFS develops a comprehensive API set that encompasses vital semantic file management functionalities. These functionalities include semantic file retrieval, file update monitoring and summarization, and semantic file rollback. By offering a diverse array of supported functions, LSFS expands the possibilities for file management, providing users with an unprecedented level of convenience.
Furthermore, LSFS implements semantic indexing to store files. By constructing semantic indexes, LSFS intelligently organizes and categorizes files based on their content. This enhances search capabilities and improves the efficiency of file operations, enabling users to find and manage their files more effectively.
Syscalls and Vector Database Integration for Intelligent Operations
At the micro-level, LSFS design and implements syscalls (system calls) for various semantic operations such as CRUD (create, read, update, delete), group by, and join. These syscalls are powered by a vector database, providing fast and efficient access to file data for intelligent file management tasks.
Enhanced Capabilities with LLM Integration
By integrating LLMs into the file management framework, LSFS introduces advanced capabilities such as content summarization and version comparison. These intelligent features further enhance the usability and power of LSFS, enabling users to efficiently handle complex file-related tasks.
Advantages of LSFS over Traditional File Systems
In experiments, LSFS has showcased significant improvements over traditional file systems in several aspects:
- User Convenience: LSFS offers an intuitive and natural language-based approach to file management, eliminating the need for deep technical knowledge.
- Diverse Functionality: The comprehensive API set of LSFS provides numerous semantic file management functions, expanding the range of operations users can perform.
- Accuracy and Efficiency: LSFS leverages LLMs and semantic indexing to enhance the accuracy and efficiency of file operations, enabling users to retrieve and manipulate files more effectively.
Overall, LSFS represents a significant step towards revolutionizing file management systems. Its integration of LLMs and semantic approaches empowers users and agents with an intuitive, intelligent, and efficient file management experience. As LLM technology continues to advance, we can expect LSFS to evolve further, introducing even more sophisticated capabilities and enhancing productivity for users across various domains.
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by jsendak | Oct 16, 2024 | Computer Science
arXiv:2410.10867v1 Announce Type: cross
Abstract: Automatic metrics are used as proxies to evaluate abstractive summarization systems when human annotations are too expensive. To be useful, these metrics should be fine-grained, show a high correlation with human annotations, and ideally be independent of reference quality; however, most standard evaluation metrics for summarization are reference-based, and existing reference-free metrics correlate poorly with relevance, especially on summaries of longer documents. In this paper, we introduce a reference-free metric that correlates well with human evaluated relevance, while being very cheap to compute. We show that this metric can also be used alongside reference-based metrics to improve their robustness in low quality reference settings.
Analysis of Automatic Metrics for Abstractive Summarization Systems
In the field of natural language processing, automatic evaluation metrics play a crucial role in assessing the quality of abstractive summarization systems. These systems are designed to generate concise and informative summaries that capture the key information from longer documents. However, the process of human annotation for evaluating summaries can be time-consuming and costly. Hence, the need for automatic metrics that can serve as proxies to measure the quality of these systems.
Traditional evaluation metrics for summarization rely on the availability of reference summaries. These metrics compare the generated summary to one or more reference summaries to assess how well the system has captured the essential information. However, this reference-based approach has limitations, especially when dealing with longer documents. The reference summaries may not cover all aspects of the document, leading to poor correlation with human evaluations.
To overcome these limitations, the authors propose a reference-free metric that focuses on relevance, which is a key factor in evaluating summaries. By measuring the overlap between the generated summary and the content of the original document, this metric provides a fine-grained evaluation of how well the summary captures the relevant information. Moreover, this metric is computationally inexpensive, making it suitable for large-scale evaluation.
The multi-disciplinary aspects of this work can be seen in various domains. Firstly, in the field of multimedia information systems, where text summarization enhances the accessibility and usability of various forms of media, such as news articles, video transcripts, and social media posts. Automatic evaluation metrics play a crucial role in assessing the quality of these summaries, which in turn impact the overall user experience.
Secondly, in the domain of animations, artificial reality, augmented reality, and virtual realities, the ability to generate coherent and informative summaries becomes even more crucial. These technologies rely on textual information to provide context and guidance to users. Automatic evaluation metrics can assist in the development and improvement of algorithms that generate summaries for these immersive experiences.
Finally, from a broader perspective, the concept of automatic evaluation metrics for abstractive summarization intersects with the field of natural language understanding and generation. The development of robust metrics that accurately capture the relevance and quality of summaries contributes to advancing the state-of-the-art in natural language processing.
In conclusion, this paper presents a novel reference-free metric for evaluating abstractive summarization systems. By focusing on relevance and incorporating a fine-grained evaluation, this metric offers a more reliable assessment of summary quality. Its low computational cost makes it highly practical for large-scale evaluation. Furthermore, the implications of this work extend beyond the field of summarization, aligning with various domains such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Oct 16, 2024 | Computer Science
Enhancing ARDS Management with High-Fidelity 3D Lung CT Imaging
Acute respiratory distress syndrome (ARDS) is a life-threatening condition that poses significant challenges for healthcare providers. With a mortality rate of around 40%, finding innovative and effective ways to assess lung pathology and monitor treatment efficacy is crucial. Traditional imaging methods, such as chest X-rays, have limitations in providing a comprehensive view of lung abnormalities in ARDS patients.
In this study, researchers have investigated the potential of three-dimensional (3D) computed tomography (CT) as a solution to overcome these limitations. Unlike traditional imaging methods, 3D CT imaging provides a more detailed and comprehensive visualization of the lungs, allowing for a thorough analysis of lung aeration, atelectasis, and the effects of therapeutic interventions.
However, the routine use of CT in ARDS management has been constrained by practical challenges. Critically ill patients often face risks associated with transporting them to remote CT scanners, making frequent imaging and monitoring difficult. To address this issue, the researchers developed a novel approach to synthesize high-fidelity 3D lung CT images from 2D generated X-ray images with associated physiological parameters.
This approach utilizes a score-based 3D residual diffusion model, which allows for the creation of high-quality 3D CT images that can be validated with ground truth. By leveraging the existing X-ray images and associated physiological parameters, healthcare providers can obtain a comprehensive view of the lungs without the need for additional invasive procedures or patient transfer.
The preliminary results of this study are highly promising, showcasing the potential of this approach in enhancing ARDS management. The high-fidelity 3D CT images generated through this method can provide detailed insights into lung pathology, aiding in more accurate diagnosis and monitoring of treatment effectiveness. Additionally, by eliminating the need for patient transport, the risks associated with imaging critically ill patients are significantly reduced.
Further research and validation are essential to fully establish the viability and effectiveness of this approach in a clinical setting. If proven successful, this technology could revolutionize the way ARDS is managed, improving patient outcomes and overall healthcare efficiency.
Expert Insight:
The development of high-fidelity 3D lung CT imaging from 2D generated X-ray images is a significant advancement in ARDS management. By leveraging existing imaging resources and associated physiological parameters, this approach provides a non-invasive and efficient method to obtain detailed information about lung pathology. The ability to visualize lung aeration, atelectasis, and the effects of therapeutic interventions in a comprehensive manner can significantly improve diagnostic accuracy and treatment planning.
Traditional imaging methods, such as chest X-rays, often lack the ability to provide the necessary level of detail for precise assessment of ARDS-related lung abnormalities. 3D CT imaging offers a solution by enabling a more in-depth analysis of lung structures and conditions. However, the challenges associated with patient transport to remote CT scanners have limited its routine application in ARDS management. This new approach addresses this limitation by synthesizing 3D CT images from readily available 2D X-ray images, eliminating the need for additional patient transfer.
The preliminary results of this study highlight the immense potential of this technology in enhancing ARDS management. High-quality 3D CT images can aid in early detection, accurate diagnosis, and continuous monitoring of treatment efficacy. The non-invasive nature of this approach reduces the risks and complications associated with patient transport and invasive procedures, providing a safer and more efficient means of assessing lung pathology in critically ill ARDS patients.
Further research and validation of this approach are crucial to ensure its efficacy and reliability in real-world ARDS cases. Additionally, exploring the integration of this technology into existing imaging systems and clinical workflows would be essential to maximize its impact on patient care. If successfully implemented, this approach could revolutionize ARDS management and significantly improve patient outcomes.
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