by jsendak | Jan 8, 2024 | Computer Science
Expert Commentary: Monotonic Relationship between Coherence of Illumination and Computer Vision Performance
The recent study presented in this article sheds light on the relationship between the degree of coherence of illumination and performance in various computer vision tasks. By simulating partially coherent illumination using computational methods, researchers were able to investigate the impact of coherent length on image entropy, object recognition, and depth sensing performance.
Understanding Coherence of Illumination
Coherence of illumination refers to the degree to which the phase relationships between different points in a lightwave are maintained. An ideal coherent lightwave has perfect phase relationships, while partially coherent lightwave exhibits some random phase variations. In computer vision, coherence of illumination plays a crucial role in determining the quality of images and the accuracy of different vision tasks.
Effect on Image Entropy
One of the interesting findings of this study is the positive correlation between increasing coherent length and improved image entropy. Image entropy represents the amount of randomness or information content in an image. Higher entropy indicates more varied and detailed features, leading to better visual representation. The researchers’ use of computational methods to mimic partially coherent illumination enabled them to observe how coherence affects image entropy.
Enhanced Object Recognition
The impact of coherence on object recognition performance is another important aspect highlighted in this study. By employing a deep neural network for object recognition tasks, the researchers found that increased coherent length led to better object recognition results. This suggests that more coherent illumination provides clearer and more distinctive visual cues, improving the model’s ability to classify and identify objects accurately.
Improved Depth Sensing Performance
In addition to object recognition, the researchers also explored the relationship between coherence of illumination and depth sensing performance. Depth sensing is crucial in applications like robotics, augmented reality, and autonomous driving. The study revealed a positive correlation between increased coherent length and enhanced depth sensing accuracy. This indicates that more coherent illumination allows for better depth estimation and reconstruction, enabling more precise understanding of a scene’s 3D structure.
Future Implications
The results of this study provide valuable insights into the importance of coherence of illumination in computer vision tasks. By further refining and understanding the relationship between coherence and performance, researchers can potentially develop novel techniques to improve computer vision systems.
For instance, the findings could be leveraged to optimize lighting conditions in imaging systems, such as cameras and sensors used for object recognition or depth sensing. Additionally, advancements in computational methods for simulating partially coherent illumination could enable more accurate modeling and analysis of real-world scenarios.
Furthermore, these findings could also guide the development of new algorithms and models that take into account the coherence of illumination, leading to more robust computer vision systems capable of handling complex visual environments.
Overall, this study paves the way for future research in understanding the interplay between coherence of illumination and computer vision performance. It opens up avenues for further exploration and innovations in the field of computer vision, with the potential to drive advancements in diverse applications such as autonomous systems, medical imaging, and surveillance.
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by jsendak | Jan 8, 2024 | Computer Science
The current landscape of research leveraging large language models (LLMs) is
experiencing a surge. Many works harness the powerful reasoning capabilities of
these models to comprehend various modalities, such as text, speech, images,
videos, etc. They also utilize LLMs to understand human intention and generate
desired outputs like images, videos, and music. However, research that combines
both understanding and generation using LLMs is still limited and in its
nascent stage. To address this gap, we introduce a Multi-modal Music
Understanding and Generation (M$^{2}$UGen) framework that integrates LLM’s
abilities to comprehend and generate music for different modalities. The
M$^{2}$UGen framework is purpose-built to unlock creative potential from
diverse sources of inspiration, encompassing music, image, and video through
the use of pretrained MERT, ViT, and ViViT models, respectively. To enable
music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging
multi-modal understanding and music generation is accomplished through the
integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA
model to generate extensive datasets that support text/image/video-to-music
generation, facilitating the training of our M$^{2}$UGen framework. We conduct
a thorough evaluation of our proposed framework. The experimental results
demonstrate that our model achieves or surpasses the performance of the current
state-of-the-art models.
The Multi-modal Music Understanding and Generation (M$^{2}$UGen) Framework: Advancing Research in Large Language Models
In recent years, research leveraging large language models (LLMs) has gained significant momentum. These models have demonstrated remarkable capabilities in understanding and generating various modalities such as text, speech, images, and videos. However, there is still a gap when it comes to combining understanding and generation using LLMs, especially in the context of music. The M$^{2}$UGen framework aims to bridge this gap by integrating LLMs’ abilities to comprehend and generate music across different modalities.
Multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are all interconnected fields that rely on the integration of different modalities to create immersive and interactive experiences. The M$^{2}$UGen framework embodies the multi-disciplinary nature of these fields by leveraging pretrained models like MERT for text understanding, ViT for image understanding, and ViViT for video understanding. By combining these models, the framework enables creative potential to be unlocked from diverse sources of inspiration.
To facilitate music generation, the M$^{2}$UGen framework utilizes AudioLDM 2 and MusicGen. These components provide the necessary tools and techniques for generating music based on the understanding obtained from LLMs. However, what truly sets M$^{2}$UGen apart is its ability to bridge multi-modal understanding and music generation through the integration of the LLaMA 2 model. This integration allows for a seamless translation of comprehended multi-modal inputs into musical outputs.
Furthermore, the MU-LLaMA model plays a crucial role in supporting the training of the M$^{2}$UGen framework. By generating extensive datasets that facilitate text/image/video-to-music generation, MU-LLaMA enables the framework to learn and improve its music generation capabilities. This training process ensures that the M$^{2}$UGen framework achieves or surpasses the performance of the current state-of-the-art models.
In the wider field of multimedia information systems, the M$^{2}$UGen framework represents a significant advancement. Its ability to comprehend and generate music across different modalities opens up new possibilities for creating immersive multimedia experiences. By combining the power of LLMs with various pretrained models and techniques, the framework demonstrates the potential for pushing the boundaries of what is possible in animations, artificial reality, augmented reality, and virtual realities.
In conclusion, the M$^{2}$UGen framework serves as a pivotal contribution to research leveraging large language models. Its integration of multi-modal understanding and music generation showcases the synergistic potential of combining different modalities. As this field continues to evolve and mature, we can expect further advancements in the realm of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 8, 2024 | Computer Science
Executive Summary
In this paper, the authors propose a novel approach to quantify execution time variability of programs using statistical dispersion parameters. They go on to discuss how this variability can be leveraged in mixed criticality real-time systems, and introduce a heuristic for computing the execution time budget for low criticality real-time tasks based on their variability. Through experiments and simulations, the authors demonstrate that their proposed heuristic reduces the probability of exceeding the allocated budget compared to algorithms that do not consider execution time variability.
Analysis and Commentary
The authors’ focus on quantifying execution time variability and its impact on real-time systems is a valuable contribution to the field. Real-time systems often have tasks with different criticality levels, and efficiently allocating execution time budgets is crucial for meeting deadlines and ensuring system reliability.
The use of statistical dispersion parameters, such as variance or standard deviation, to quantify execution time variability is a sensible approach. By considering the spread of execution times, rather than just the average or worst case, the proposed method captures a more comprehensive view of program behavior. This helps in decision-making related to resource allocations and scheduling.
The introduction of a heuristic for computing execution time budgets based on variability is a practical solution. By considering each task’s execution time variability, the proposed heuristic can allocate more accurate and realistic budgets. This reduces the probability of exceeding budgets and helps prevent performance degradation or missed deadlines in mixed criticality contexts.
The experiments and simulations conducted by the authors provide objective evidence of the benefits of incorporating execution time variability into budget allocation decisions. By comparing their proposed heuristic with other existing algorithms that disregard variability, the authors demonstrate that their approach leads to a lower probability of exceeding budgets. This supports their claim that considering variability improves system reliability and performance.
Potential Future Directions
The research presented in this paper opens up several potential future directions for exploration and enhancement:
- Integration with formal verification techniques: While the proposed heuristic shows promising results, further work could be done to integrate it with formal verification techniques. By combining the quantification of execution time variability with formal methods, it would be possible to provide stronger guarantees and proofs of correctness for real-time systems.
- Adaptive budget allocation: The current heuristic computes static budgets based on a task’s execution time variability. However, future research could explore adaptive approaches where budgets are dynamically adjusted based on real-time observations of task execution times. This could improve resource utilization and adapt to changing system conditions.
- Consideration of other factors: While execution time variability is an important factor, there are other aspects that can impact real-time systems’ performance and reliability, such as cache effects or inter-task dependencies. Future work could investigate how to incorporate these additional factors into budget allocation decisions to further enhance system behavior.
Conclusion
The paper presents a valuable contribution in the field of mixed criticality real-time systems by proposing a method to quantify execution time variability using statistical dispersion parameters. The introduction of a heuristic for allocating execution time budgets based on this variability improves system reliability and reduces the probability of exceeding budgets. The experiments and simulations conducted provide empirical evidence supporting the benefits of considering execution time variability. The research opens up potential future directions for further exploration and enhancement, including integration with formal verification techniques, adaptive budget allocation, and consideration of other factors that affect real-time system performance.
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by jsendak | Jan 8, 2024 | Computer Science
Despite recent progress in text-to-audio (TTA) generation, we show that the
state-of-the-art models, such as AudioLDM, trained on datasets with an
imbalanced class distribution, such as AudioCaps, are biased in their
generation performance. Specifically, they excel in generating common audio
classes while underperforming in the rare ones, thus degrading the overall
generation performance. We refer to this problem as long-tailed text-to-audio
generation. To address this issue, we propose a simple retrieval-augmented
approach for TTA models. Specifically, given an input text prompt, we first
leverage a Contrastive Language Audio Pretraining (CLAP) model to retrieve
relevant text-audio pairs. The features of the retrieved audio-text data are
then used as additional conditions to guide the learning of TTA models. We
enhance AudioLDM with our proposed approach and denote the resulting augmented
system as Re-AudioLDM. On the AudioCaps dataset, Re-AudioLDM achieves a
state-of-the-art Frechet Audio Distance (FAD) of 1.37, outperforming the
existing approaches by a large margin. Furthermore, we show that Re-AudioLDM
can generate realistic audio for complex scenes, rare audio classes, and even
unseen audio types, indicating its potential in TTA tasks.
Addressing Bias in Text-to-Audio Generation: A Multi-Disciplinary Approach
As technology continues to advance, text-to-audio (TTA) generation has seen significant progress. However, it is crucial to acknowledge the biases that can emerge when state-of-the-art models, like AudioLDM trained on imbalanced class distribution datasets such as AudioCaps, are used. This article introduces the concept of long-tailed text-to-audio generation, where models excel in generating common audio classes but struggle with rare ones, impacting the overall performance.
To combat this issue, the authors propose a retrieval-augmented approach for TTA models. The process involves leveraging a Contrastive Language Audio Pretraining (CLAP) model to retrieve relevant text-audio pairs based on an input text prompt. The features of the retrieved audio-text data then guide the learning of TTA models. By enhancing AudioLDM with this approach, the researchers introduce Re-AudioLDM, which achieves a state-of-the-art Frechet Audio Distance (FAD) of 1.37 on the AudioCaps dataset.
This work stands at the intersection of multiple disciplines, showcasing its multi-disciplinary nature. Firstly, it draws upon natural language processing techniques to retrieve relevant text-audio pairs using the CLAP model. Secondly, it leverages machine learning methodologies to enhance TTA models with the retrieved audio-text data. Finally, it applies evaluation metrics from the field of multimedia information systems, specifically Frechet Audio Distance, to assess the performance of Re-AudioLDM.
The relevance of this research to multimedia information systems lies in its aim to improve the generation performance of TTA models. Generating realistic audio for complex scenes, rare audio classes, and even unseen audio types holds great potential for various multimedia applications. For instance, in animations, artificial reality, augmented reality, and virtual realities, the ability to generate high-quality and diverse audio content is crucial for creating immersive experiences. By addressing bias in TTA generation, Re-AudioLDM opens up new possibilities for enhancing multimedia systems across these domains.
In conclusion, the proposed retrieval-augmented approach presented in this article showcases the potential to address bias in text-to-audio generation. Despite the challenges posed by imbalanced class distribution datasets, Re-AudioLDM demonstrates state-of-the-art performance and the ability to generate realistic audio across different scenarios. Moving forward, further research in this area could explore the application of similar approaches to other text-to-multimedia tasks, paving the way for more inclusive and accurate multimedia content creation.
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by jsendak | Jan 8, 2024 | Computer Science
Expert Commentary: Evaluating Image Classification Models with Automated Error Classification
This article discusses the limitations of using top-1 accuracy as a measure of progress in computer vision research and proposes a new framework for automated error classification. The authors argue that the ImageNet dataset, which has been widely used in computer vision research, suffers from significant label noise and ambiguity, making top-1 accuracy an insufficient measure.
The authors highlight that recent work employed human experts to manually categorize classification errors, but this process is time-consuming, prone to inconsistencies, and requires trained experts. Therefore, they propose an automated error classification framework as a more practical and scalable solution.
The framework developed by the authors allows for the comprehensive evaluation of the error distribution across over 900 models. Surprisingly, the study finds that top-1 accuracy remains a strong predictor for the portion of all error types across different model architectures, scales, and pre-training corpora. This suggests that while top-1 accuracy may underreport a model’s true performance, it still provides valuable insights.
This research is significant because it tackles an important challenge in computer vision research – evaluating models beyond top-1 accuracy. The proposed framework allows researchers to gain deeper insights into the specific types of errors that models make and how different modeling choices affect error distributions.
The release of their code also adds value to the research community by enabling others to replicate and build upon their findings. This level of transparency and reproducibility is crucial for advancing the field.
Implications for Future Research
This study opens up new avenues for future research in computer vision. By providing an automated error classification framework, researchers can focus on understanding and addressing specific types of errors rather than solely aiming for higher top-1 accuracy.
The findings also raise questions about the relationship between model architecture, dataset scale, and error distributions. Further investigation in these areas could help identify patterns or factors that contribute to different types of errors. This knowledge can guide the development of improved models and datasets.
Additionally, the study’s emphasis on the usefulness of top-1 accuracy, despite its limitations, suggests that it is still a valuable metric for evaluating model performance. Future research could explore ways to improve upon top-1 accuracy or develop alternative metrics that capture the nuances of error distributions more effectively.
Conclusion
The proposed automated error classification framework addresses the limitations of using top-1 accuracy as a measure of progress in computer vision research. By comprehensively evaluating error distributions across various models, the study highlights the relationship between top-1 accuracy and different types of errors.
This research not only provides insights into the challenges of image classification but also offers a valuable tool for assessing model performance and investigating the impact of modeling choices on error distributions.
As the field of computer vision continues to advance, this study sets the stage for more nuanced evaluation methodologies, leading to more robust and accurate models in the future.
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by jsendak | Jan 8, 2024 | Computer Science
In times of emergency, crisis response agencies need to quickly and
accurately assess the situation on the ground in order to deploy relevant
services and resources. However, authorities often have to make decisions based
on limited information, as data on affected regions can be scarce until local
response services can provide first-hand reports. Fortunately, the widespread
availability of smartphones with high-quality cameras has made citizen
journalism through social media a valuable source of information for crisis
responders. However, analyzing the large volume of images posted by citizens
requires more time and effort than is typically available. To address this
issue, this paper proposes the use of state-of-the-art deep neural models for
automatic image classification/tagging, specifically by adapting
transformer-based architectures for crisis image classification (CrisisViT). We
leverage the new Incidents1M crisis image dataset to develop a range of new
transformer-based image classification models. Through experimentation over the
standard Crisis image benchmark dataset, we demonstrate that the CrisisViT
models significantly outperform previous approaches in emergency type, image
relevance, humanitarian category, and damage severity classification.
Additionally, we show that the new Incidents1M dataset can further augment the
CrisisViT models resulting in an additional 1.25% absolute accuracy gain.
In this article, we delve into the use of deep neural models for automatic image classification and tagging in the context of crisis response. During emergencies, crisis response agencies often face a lack of timely and comprehensive information, hindering their ability to make informed decisions. However, citizen journalism through social media has emerged as a valuable source of data, particularly through the widespread use of smartphones with high-quality cameras.
The challenge lies in analyzing the large volume of images posted by citizens, which can be a time-consuming and resource-intensive task. To address this, the authors propose the use of state-of-the-art deep neural models, specifically transformer-based architectures, for crisis image classification. They develop and test a range of models using the Incidents1M crisis image dataset, showcasing the effectiveness of these models in various classification tasks such as emergency type, image relevance, humanitarian category, and damage severity.
The adoption of transformer-based architectures, such as CrisisViT, in crisis image classification signifies the multi-disciplinary nature of this concept. By leveraging advancements in deep learning and computer vision, these models enable automated analysis of crisis-related images, augmenting the capabilities of crisis response agencies.
From a broader perspective, this content aligns closely with the field of multimedia information systems. Multimedia refers to the integration of different forms of media like images, videos, and audio. The analysis of crisis-related images falls under this purview, contributing to the development of more comprehensive multimedia information systems for crisis response.
Furthermore, the article highlights the relevance of artificial reality technologies such as augmented reality (AR) and virtual reality (VR) in crisis response. These technologies enable users to immerse themselves in simulated crisis scenarios and gain valuable experience without being physically present. The accuracy and efficiency gained from improving crisis image classification can enhance the realism and effectiveness of AR and VR-based training programs for first responders and crisis management professionals.
Overall, this research showcases the power of deep neural models in automating crisis image analysis and classification. By leveraging transformer-based architectures and datasets like Incidents1M, significant improvements in accuracy and efficiency can be achieved. These advancements contribute to the wider field of multimedia information systems, as well as align closely with the applications of artificial reality technologies in crisis response.
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