by jsendak | Mar 28, 2024 | Computer Science
arXiv:2403.17420v1 Announce Type: new
Abstract: The goal of the multi-sound source localization task is to localize sound sources from the mixture individually. While recent multi-sound source localization methods have shown improved performance, they face challenges due to their reliance on prior information about the number of objects to be separated. In this paper, to overcome this limitation, we present a novel multi-sound source localization method that can perform localization without prior knowledge of the number of sound sources. To achieve this goal, we propose an iterative object identification (IOI) module, which can recognize sound-making objects in an iterative manner. After finding the regions of sound-making objects, we devise object similarity-aware clustering (OSC) loss to guide the IOI module to effectively combine regions of the same object but also distinguish between different objects and backgrounds. It enables our method to perform accurate localization of sound-making objects without any prior knowledge. Extensive experimental results on the MUSIC and VGGSound benchmarks show the significant performance improvements of the proposed method over the existing methods for both single and multi-source. Our code is available at: https://github.com/VisualAIKHU/NoPrior_MultiSSL
Expert Commentary: Advancements in Multi-Sound Source Localization
Multi-sound source localization is a crucial task in the field of multimedia information systems, as it enables the identification and localization of sound sources in a given environment. The ability to accurately localize sound sources has wide-ranging applications, including audio scene analysis, surveillance systems, and virtual reality experiences.
The mentioned article introduces a novel method for multi-sound source localization that overcomes the limitation of requiring prior knowledge about the number of sound sources to be separated. This is a significant advancement, as it allows for more flexible and adaptable localization in real-world scenarios where prior information is often unavailable.
One notable feature of the proposed method is the iterative object identification (IOI) module. This module leverages an iterative approach to identify sound-making objects in the mixture. By iteratively refining the object identification process, the method can improve the accuracy of localization without the need for prior knowledge. This iterative approach is a testament to the multi-disciplinary nature of this research, combining concepts from signal processing, machine learning, and computer vision.
To further enhance the accuracy of localization, the authors introduce the object similarity-aware clustering (OSC) loss. This loss function guides the IOI module to effectively combine regions of the same object while also distinguishing between different objects and backgrounds. By incorporating object similarity awareness into the clustering process, the proposed method achieves better discrimination and localization performance.
The experimental results on the MUSIC and VGGSound benchmarks demonstrate the significant performance improvements of the proposed method over existing methods for both single and multi-source localization. This suggests that the method can accurately identify and localize sound sources in various scenarios, making it suitable for real-world applications.
In the wider field of multimedia information systems, the advancements in multi-sound source localization have implications for the fields of animations, artificial reality, augmented reality, and virtual realities. Accurate localization of sound sources in these contexts can greatly enhance the immersive experiences and realism of multimedia content. For example, in virtual reality applications, precise localization of virtual sound sources can create a more realistic and engrossing environment for users.
In conclusion, the proposed method for multi-sound source localization without prior knowledge in the mentioned article showcases the continual progress in the field of multimedia information systems. The multi-disciplinary nature of this research, alongside the significant performance improvements, paves the way for enhanced multimedia experiences in various domains, including animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 28, 2024 | Computer Science
Artificial intelligence (AI) systems are being increasingly used in the medical field to assist in diagnosis and treatment decisions. However, one of the challenges in evaluating the performance of these AI systems is the lack of ground-truth annotations in real-world data. This means that when the AI system is deployed in a clinical setting and encounters data that is different from the data it was trained on, it may not perform as expected.
In this article, the authors introduce a framework called SUDO, which stands for Supervised to Unsupervised Data Optimization. SUDO addresses the issue of evaluating AI systems without ground-truth annotations by assigning temporary labels to data points in the wild. The temporary labels are then used to train multiple models, and the model with the highest performance is considered to have the most likely label.
The authors conducted experiments using AI systems developed for dermatology images, histopathology patches, and clinical reports. They found that SUDO can reliably assess model performance and identify unreliable predictions. By triaging unreliable predictions for further inspection, SUDO can help improve the integrity of research findings and the deployment of ethical AI systems in medicine.
One of the key benefits of SUDO is its ability to assess algorithmic bias in AI systems without ground-truth annotations. Algorithmic bias, where an AI system produces unfair or discriminatory outcomes, is a growing concern in healthcare. By using SUDO to evaluate algorithmic bias, researchers and developers can gain insights into potential biases in AI systems and take steps to address them.
This framework has the potential to significantly enhance the evaluation and deployment of AI systems in the medical field. By providing a reliable proxy for model performance and enabling the assessment of algorithmic bias, SUDO can help ensure the safety, reliability, and ethical use of AI systems in healthcare.
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by jsendak | Mar 26, 2024 | Computer Science
arXiv:2403.16951v1 Announce Type: new
Abstract: Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low-latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client-based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.
Expert Commentary:
This article discusses the challenges faced by multimedia applications, specifically video streaming services, in terms of network load and delivery techniques. With the increasing popularity of high-resolution and low-latency video streaming, there is a need to ensure sufficient bandwidth and minimize delays. Content Delivery Networks (CDNs) have been utilized to support these streaming scenarios and provide scalable video streaming services.
However, as the demand for streaming services continues to grow and involve millions of users, CDNs need to become more efficient. This is where the concept of Network-Assisted Video Streaming (NAVS) methods comes into play. By incorporating in-network components with a broader view of the network, NAVS solutions can enhance the performance of HTTP-based adaptive streaming (HAS) algorithms used by clients.
The multi-disciplinary nature of this concept lies in the combination of networking techniques and multimedia information systems. It is not just about optimizing delivery techniques, but also considering the overall network infrastructure to improve the quality of video streaming services.
This article highlights the importance of adopting emerging networking techniques and implementing NAVS solutions to address the bandwidth and delay requirements of modern video streaming services. It is a step forward in the evolution of multimedia systems, as it combines the fields of networking, multimedia, and information systems.
In relation to animations, artificial reality, augmented reality, and virtual realities, the concept of NAVS can play a significant role in enhancing the delivery of multimedia content in these scenarios. As these technologies heavily rely on real-time and high-quality streaming, optimizing the network infrastructure through NAVS solutions can greatly improve the overall user experience.
Overall, the article brings attention to the need for efficient content delivery in multimedia applications and proposes the adoption of NAVS methods as a solution. By incorporating networking techniques and considering the wider context of the network, it aims to improve video streaming services and meet the growing demands of the industry.
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by jsendak | Mar 26, 2024 | Computer Science
Analysis of the Study on Detecting Psychological Stress from Persian Tweets
As an expert commentator, I will delve into the details and implications of a study that focuses on detecting psychological stress related to suicide from Persian tweets using learning based methods. The study highlights the significance of identifying psychological stressors in an at-risk population, as it can potentially contribute to early prevention of suicidal behaviors.
The researchers acknowledge the growing popularity and widespread use of social media platforms, particularly Twitter, as a means of real-time information sharing. This provides a unique opportunity for early intervention and detection of psychological stressors in both large and small populations. However, most of the existing research in this area has focused on non-Persian languages, thereby limiting the applicability of the findings to Persian-speaking individuals.
The proposed approach in this study utilizes a capsule-based method to extract and classify psychological stressors from Persian tweets. Capsule networks have shown promise in various natural language processing tasks, and their application in this context can potentially yield valuable insights.
The results of the study reveal a binary classification accuracy of 0.83, indicating that the capsule-based approach is effective in detecting psychological stress related to suicide in Persian tweets. This level of accuracy is promising and suggests the potential usefulness of machine learning techniques in identifying individuals at risk of suicidal tendencies.
By training the model on a large dataset of Persian tweets, the researchers have been able to achieve a relatively high accuracy in detecting psychological stress. This highlights the importance of utilizing a comprehensive and diverse dataset to develop robust machine learning models.
Further research in this area could focus on refining the capsule-based approach and exploring additional linguistic features specific to Persian tweets that could enhance the accuracy of the classification. Additionally, investigating the generalizability of the model to other Persian-speaking populations in different cultural contexts would be a valuable direction for future studies.
In conclusion, this study demonstrates the potential of utilizing learning based methods, specifically capsule networks, to detect psychological stress from Persian tweets. The findings contribute to the field of suicide prevention by highlighting the importance of early intervention and leveraging social media platforms for identifying individuals at risk of suicidal behaviors. Further research is needed to refine and expand upon these techniques for better detection and prevention of suicide in Persian-speaking populations.
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by jsendak | Mar 25, 2024 | Computer Science
arXiv:2403.15226v1 Announce Type: new
Abstract: In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates the attention redundancy and skips the less important MHAs to speed up inference. Besides, we also propose a novel propagation-of-information adapter (PIA) to serve the attention skipping of EAS and keep parameter efficiency, which can be further re-parameterized into feed-forward networks (FFNs) for zero-extra latency. To validate EAS, we apply it to a recently proposed MLLM called LaVIN and a classic VL pre-trained model called METER, and conduct extensive experiments on a set of benchmarks. The experiments show that EAS not only retains high performance and parameter efficiency, but also greatly speeds up inference speed. For instance, LaVIN-EAS can obtain 89.98% accuracy on ScineceQA while speeding up inference by 2.2 times to LaVIN
Efficient Attention Skipping (EAS): Enhancing Multi-modal Large Language Models
In the field of multimedia information systems, there has been significant interest in developing more efficient and effective methods for processing large language models. These models, known as Multi-modal Large Language Models (MLLMs), have shown promise in various applications such as natural language processing, image captioning, and question answering.
One of the main computational overheads of MLLMs is the use of multi-head attentions (MHAs), which are responsible for capturing and weighing the importance of different input modalities. However, recent research has revealed that these MHAs can often be redundant or less important for downstream tasks.
In this paper, the authors propose a novel parameter and computation efficient tuning method for MLLMs, termed Efficient Attention Skipping (EAS). The core idea behind EAS is to evaluate the attention redundancy and skip the less important MHAs in order to speed up inference.
To support the attention skipping process, the authors also introduce a novel propagation-of-information adapter (PIA) that ensures parameter efficiency. This adapter can be re-parameterized into feed-forward networks (FFNs) with zero-extra latency, further optimizing the computational efficiency of the model.
The authors validate the effectiveness of EAS by applying it to two different MLLMs: LaVIN, a recently proposed model, and METER, a classic vision and language pre-trained model. They conduct extensive experiments on a set of benchmarks and evaluate the performance and speed of the models with and without EAS.
The results of the experiments demonstrate that EAS not only retains high performance and parameter efficiency but also significantly speeds up the inference process. For example, LaVIN-EAS achieves 89.98% accuracy on the ScineceQA benchmark while speeding up inference by 2.2 times compared to LaVIN without EAS.
This research showcases the multi-disciplinary nature of the concepts discussed. It combines elements from natural language processing, computer vision, and machine learning to optimize the performance of MLLMs. The efficiency gained through attention skipping and the use of propagation-of-information adapters can greatly enhance the usability of MLLMs in real-world applications.
In the wider field of multimedia information systems, techniques like Efficient Attention Skipping and the advancements made in MLLMs contribute to the development of more efficient and effective multimedia processing algorithms. These algorithms can be utilized in various multimedia applications, such as virtual reality and augmented reality systems, where the real-time processing of both textual and visual information is crucial.
Overall, this research presents a significant step forward in the optimization of MLLMs and paves the way for future advancements in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 25, 2024 | Computer Science
Socioeconomic Bias in Large Language Models: Understanding the Impact
Socioeconomic bias is a pervasive issue in society that perpetuates systemic inequalities and hinders inclusive progress. It influences access to opportunities and resources based on individuals’ economic and social backgrounds. In this paper, the researchers delve into the presence of socioeconomic bias in large language models, shedding light on its implications and potential consequences.
Introducing the SilverSpoon Dataset
To investigate the presence of socioeconomic bias in large language models, the researchers introduce a novel dataset called SilverSpoon. This dataset consists of 3000 hypothetical scenarios that depict underprivileged individuals performing ethically ambiguous actions due to their circumstances. The researchers then annotate these scenarios using a dual-labeling scheme, with annotations from individuals belonging to both ends of the socioeconomic spectrum.
By creating such a dataset, the researchers are able to analyze how large language models respond to these scenarios and evaluate the degree of socioeconomic bias expressed by these models. This allows for a deeper understanding of the biases that may exist in these models and their potential effects.
Evaluating Socioeconomic Bias in Large Language Models
Using the SilverSpoon dataset, the researchers evaluate the degree of socioeconomic bias expressed in large language models, and how this degree varies with the size of the model. The aim is to determine whether these models are capable of empathizing with the socioeconomically underprivileged across a range of scenarios.
Interestingly, the analysis reveals a discrepancy between human perspectives on ethically justified actions involving the underprivileged. Different individuals possess varying levels of empathy toward the underprivileged in different situations. However, regardless of the situation, most large language models fail to empathize with the socioeconomically underprivileged.
This finding raises questions about the training data and algorithms used in the development of these language models. It highlights the need for further research into the nature of this bias and its implications.
Qualitative Analysis and Implications
In addition to evaluating the degree of bias, the researchers perform a qualitative analysis to understand the nature of the socioeconomic bias expressed by large language models. This analysis sheds light on the underlying factors that contribute to this bias and provides insight into potential avenues for addressing it.
The existence of socioeconomic bias in large language models has significant implications. These models play a crucial role in various applications, such as natural language processing and content generation. If these models fail to empathize with the socioeconomically underprivileged, they risk perpetuating and amplifying existing inequalities in society.
Fostering Further Research
To further advance research in this domain, the researchers make the SilverSpoon dataset and their evaluation harness publicly available. This move encourages other researchers to explore the issue of socioeconomic bias in language models and potentially develop strategies to mitigate and address this bias.
Overall, this study provides valuable insights into the presence of socioeconomic bias in large language models. It highlights the need for increased awareness and scrutiny regarding the biases embedded in these models and the importance of working towards more inclusive and equitable AI technology.
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