by jsendak | Jan 1, 2024 | Computer Science
The quality of a face crop in an image is decided by many factors such as
camera resolution, distance, and illumination condition. This makes the
discrimination of face images with different qualities a challenging problem in
realistic applications. However, most existing approaches are designed
specifically for high-quality (HQ) or low-quality (LQ) images, and the
performances would degrade for the mixed-quality images. Besides, many methods
ask for pre-trained feature extractors or other auxiliary structures to support
the training and the evaluation. In this paper, we point out that the key to
better understand both the HQ and the LQ images simultaneously is to apply
different learning methods according to their qualities. We propose a novel
quality-guided joint training approach for mixed-quality face recognition,
which could simultaneously learn the images of different qualities with a
single encoder. Based on quality partition, classification-based method is
employed for HQ data learning. Meanwhile, for the LQ images which lack identity
information, we learn them with self-supervised image-image contrastive
learning. To effectively catch up the model update and improve the
discriminability of contrastive learning in our joint training scenario, we
further propose a proxy-updated real-time queue to compose the contrastive
pairs with features from the genuine encoder. Experiments on the low-quality
datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five
high-quality datasets demonstrate the effectiveness of our proposed approach in
recognizing face images of different qualities.
Improving Mixed-Quality Face Recognition with Quality-Guided Joint Training
In the field of multimedia information systems, face recognition has always been a challenging problem, particularly when dealing with mixed-quality face images. The quality of a face crop in an image is influenced by various factors, including camera resolution, distance, and illumination condition. Discriminating face images with different qualities poses a difficult task in realistic applications.
Traditional approaches to face recognition have been designed specifically for either high-quality (HQ) or low-quality (LQ) images. However, when applied to mixed-quality images, these approaches tend to perform poorly. Moreover, many existing methods require pre-trained feature extractors or auxiliary structures to support training and evaluation.
In this paper, the authors propose a novel quality-guided joint training approach for mixed-quality face recognition. The key idea is to apply different learning methods based on the qualities of the images. This approach enables simultaneous learning of HQ and LQ images using a single encoder.
For HQ data learning, a classification-based method is employed based on quality partitioning. This allows for better understanding and interpretation of HQ images. On the other hand, LQ images lack identity information, so the authors propose learning them using self-supervised image-image contrastive learning.
To address the challenge of model update and improve the discriminability of contrastive learning in the joint training scenario, the authors propose a proxy-updated real-time queue. This queue is used to compose contrastive pairs with features from the genuine encoder. This ensures that the model keeps up with updates and enhances the effectiveness of contrastive learning.
The proposed approach is evaluated using various datasets, including low-quality datasets such as SCface and Tinyface, a mixed-quality dataset called IJB-B, and five high-quality datasets. The experiments demonstrate the effectiveness of the proposed approach in recognizing face images of different qualities.
Multi-disciplinary Nature and Related Concepts
This research on mixed-quality face recognition combines concepts and techniques from various disciplines. It leverages principles from computer vision, machine learning, and multimedia information systems to address the challenge of discriminating face images with different qualities.
Furthermore, this study is closely related to the broader field of multimedia information systems, as it deals with the analysis and understanding of visual content, specifically face images. It incorporates techniques for image quality assessment, feature extraction, and learning methods to improve the recognition of face images of different qualities.
In addition, the proposed approach has implications for animations, artificial reality, augmented reality, and virtual realities. Face recognition is a fundamental component in these domains, and advancements in mixed-quality face recognition can enhance the realism and accuracy of facial animations and virtual environments. By applying different learning methods according to image qualities, the proposed approach contributes to improving the overall quality and fidelity of multimedia systems involving virtual representations of human faces.
Overall, this research presents a novel quality-guided joint training approach for mixed-quality face recognition. It demonstrates the importance of considering different learning methods based on image qualities to achieve better performance. With its multidisciplinary nature and relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this study opens up new possibilities for advancing face recognition technologies and enhancing various applications in visual computing.
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by jsendak | Jan 1, 2024 | Computer Science
Generalization in supervised learning of single-channel speech enhancement
In the field of supervised learning for single-channel speech enhancement, generalization has always been a major challenge. It is crucial for models to perform well not only on the training data but also on unseen data. In this article, we will discuss a new approach called Learnable Loss Mixup (LLM) that addresses this issue and improves the generalization of deep learning-based speech enhancement models.
Loss mixup is a technique that involves optimizing a mixture of loss functions of random sample pairs to train a model on virtual training data constructed from these pairs. It has been shown to be effective in improving generalization performance in various domains. Learnable loss mixup is a special variant of loss mixup, where the loss functions are mixed using a non-linear mixing function that is automatically learned via neural parameterization and conditioned on the mixed data.
The authors of this work conducted experiments on the VCTK benchmark, which is widely used for evaluating speech enhancement algorithms. The results showed that learnable loss mixup achieved a PESQ score of 3.26, outperforming the state-of-the-art models.
This is a significant improvement in performance and demonstrates the effectiveness of the learnable loss mixup approach. By incorporating the mixed data and using a non-linear mixing function learned through neural parameterization, the model is able to better capture the complexities and variations present in real-world speech data. This enables it to generalize well on unseen data and perform better than existing models.
The success of learnable loss mixup opens up possibilities for further research and development in the field of supervised learning for single-channel speech enhancement. Future work could explore different methods for non-linear mixing function parameterization and investigate its impact on generalization performance. Additionally, it would be interesting to evaluate the performance of learnable loss mixup on other benchmark datasets and compare it against other state-of-the-art models in the field.
In conclusion, learnable loss mixup is a promising technique for improving the generalization of deep learning-based speech enhancement models. Its ability to automatically learn a non-linear mixing function through neural parameterization allows it to capture the nuances of real-world speech data and outperform existing approaches. This work contributes to advancing the field of supervised learning for single-channel speech enhancement and paves the way for future research in this area.
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by jsendak | Jan 1, 2024 | Computer Science
Audio Question Answering (AQA) constitutes a pivotal task in which machines
analyze both audio signals and natural language questions to produce precise
natural language answers. The significance of possessing high-quality, diverse,
and extensive AQA datasets cannot be overstated when aiming for the precision
of an AQA system. While there has been notable focus on developing accurate and
efficient AQA models, the creation of high-quality, diverse, and extensive
datasets for the specific task at hand has not garnered considerable attention.
To address this challenge, this work makes several contributions. We introduce
a scalable AQA data generation pipeline, denoted as the AQUALLM framework,
which relies on Large Language Models (LLMs). This framework utilizes existing
audio-caption annotations and incorporates state-of-the-art LLMs to generate
expansive, high-quality AQA datasets. Additionally, we present three extensive
and high-quality benchmark datasets for AQA, contributing significantly to the
progression of AQA research. AQA models trained on the proposed datasets set
superior benchmarks compared to the existing state-of-the-art. Moreover, models
trained on our datasets demonstrate enhanced generalizability when compared to
models trained using human-annotated AQA data. Code and datasets will be
accessible on GitHub~footnote{url{https://github.com/swarupbehera/AQUALLM}}.
Audio Question Answering (AQA) is a challenging task in which AI systems analyze both audio signals and natural language questions to generate accurate natural language answers. To ensure the precision of AQA systems, it is crucial to have high-quality, diverse, and extensive datasets specifically tailored for AQA. However, the creation of such datasets has not received much attention compared to the development of accurate AQA models.
This work addresses this challenge by introducing the AQUALLM framework, a scalable AQA data generation pipeline. This framework leverages Large Language Models (LLMs) and utilizes existing audio-caption annotations to generate expansive and high-quality AQA datasets. By incorporating state-of-the-art LLMs, the AQUALLM framework can produce datasets that significantly contribute to the progression of AQA research.
In addition to the framework, this work also presents three benchmark datasets for AQA. These datasets are extensive and of high quality, raising the bar for AQA research. AQA models trained on these datasets outperform existing state-of-the-art models, demonstrating their superiority. Furthermore, models trained using the proposed datasets show enhanced generalizability in comparison to models trained on human-annotated AQA data.
The multi-disciplinary nature of this work is evident in its use of both audio signal analysis and natural language processing techniques. By combining these disciplines, the AQUALLM framework enables the generation of comprehensive AQA datasets that capture the complexities of audio understanding and question answering.
This work also has significant implications for multimedia information systems. With the proliferation of audio content in various domains, such as podcasts, voice assistants, and audio recordings, the ability to extract information and provide accurate answers from audio becomes increasingly important. AQA systems built upon the datasets and frameworks presented here can greatly enhance the capabilities of multimedia information systems.
Furthermore, this work aligns with the fields of Animations, Artificial Reality, Augmented Reality, and Virtual Realities (AR/VR). Given the immersive nature of AR/VR experiences, the ability to interact with audio-based content becomes crucial. AQA systems that can understand and answer audio questions provide users with a more immersive and interactive AR/VR experience.
In conclusion, this article highlights the importance of high-quality AQA datasets and introduces the AQUALLM framework for generating such datasets. The benchmark datasets presented here raise the bar for AQA research and demonstrate the potential for models trained on these datasets to outperform existing state-of-the-art models. The multi-disciplinary nature of this work and its relevance to multimedia information systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities make it a significant contribution to the field.
Code and datasets: Accessible on GitHub: https://github.com/swarupbehera/AQUALLM
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by jsendak | Jan 1, 2024 | Computer Science
Statistical significance testing is a crucial component of natural language processing (NLP) research and experimentation. Its purpose is to determine whether the results observed in a study or experiment are likely to be due to chance or if they represent a genuine relationship or effect. One of the key aspects of significance testing is the estimation of confidence intervals, which rely on sample variances.
In most cases, calculating sample variance is relatively straightforward when comparing against a known ground truth. However, in NLP tasks, it is common to utilize metric models for evaluation purposes. This means that instead of comparing against ground truth, we compare against the outputs of a metric model, like a toxicity classifier.
Traditionally, existing research and methodologies overlook the potential variance change that can arise due to the errors produced by the metric model. As a consequence, this oversight can lead to incorrect conclusions and a misinterpretation of the significance of the results obtained.
This work addresses this issue by establishing a solid mathematical foundation for conducting significance testing when utilizing metric models for evaluation in NLP tasks. Through experiments conducted on public benchmark datasets and a production system, the researchers demonstrate the impact of considering metric model errors in calculating sample variances for model-based metrics.
The findings of this study highlight that not accounting for metric model errors can yield erroneous conclusions in certain experiments. By properly incorporating these errors into the calculations, researchers and practitioners can more accurately assess the significance of their results and draw appropriate conclusions.
Expert Analysis:
Significance testing is a critical aspect of any scientific research, including NLP. However, it is often overlooked that NLP tasks frequently rely on metric models for evaluation, rather than comparing against an absolute ground truth. This introduces an additional layer of uncertainty and potential error that needs to be accounted for in significance testing.
The authors of this work have taken a step in the right direction by recognizing the need to consider metric model errors in the calculation of sample variances. By conducting experiments on both public benchmark datasets and a real-world production system, they provide empirical evidence of the impact that this consideration can have on the conclusions drawn from NLP experiments.
While this study is a significant contribution, it is important to acknowledge that there may be limitations in its scope. The specific findings and conclusions might be specific to the datasets and metric models used in the experiments. Therefore, it would be beneficial to replicate these experiments in different contexts to assess the generalizability of the results.
Additionally, future research could focus on developing more robust methodologies for incorporating metric model errors into significance testing in NLP. This could potentially involve leveraging techniques from uncertainty quantification and propagation to obtain more accurate estimates of sample variances.
Overall, this work serves as an important reminder that statistical significance testing in NLP should not overlook the influence of metric model errors. By considering these errors and adapting the calculation of sample variances accordingly, researchers can ensure that their conclusions accurately reflect the true nature of their results.
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by jsendak | Jan 1, 2024 | Computer Science
Analysis: Deep Learning for Quantitative Analysis of Carbide Precipitates in Steels
The use of deep learning techniques to segment scanning electron microscope (SEM) images and analyze carbide precipitates in steels is a significant advancement in the field of microstructure analysis. This study reveals valuable insights into the volume percentage, size distribution, and orientations of carbides in lower bainite and tempered martensite steels.
One key finding is that lower bainite and tempered martensite exhibit similar volume percentages of carbides. This suggests that the presence of carbide precipitates contributes to the overall strength of these steels, regardless of the specific microstructure. However, the distribution of carbides differs between the two microstructures, with tempered martensite showing a more uniform distribution.
Another interesting observation is the alignment of carbides. In lower bainite, the carbides demonstrate a tendency for better alignment compared to tempered martensite, which aligns with previous research findings. This alignment could potentially affect the mechanical properties of the materials, such as crack propagation and fracture resistance.
Despite the differences in distribution and alignment, both microstructures exhibit a scattered orientation of carbides without any discernible pattern. This suggests that other factors, such as grain boundaries and crystallographic orientations, might influence the arrangement of carbides within these steels.
The comparative analysis of aspect ratios and sizes of carbides in lower bainite and tempered martensite reveals striking similarities. This suggests that the formation and growth mechanisms of carbides are similar across these two microstructures. Understanding these mechanisms is crucial for optimizing the heat treatment processes and improving the overall performance of steels.
The deep learning model utilized in this study achieves an impressive pixelwise accuracy of 98.0% in classifying carbide/iron matrix at the individual pixel level. This high accuracy demonstrates the potential of deep learning for microstructure analysis and its ability to provide time-efficient and versatile workflows for quantitative analysis of secondary phases in various materials.
In conclusion, this study highlights the significant role of deep learning techniques in advancing microstructure analysis. The insights gained from the segmentation and analysis of carbide precipitates in lower bainite and tempered martensite steels contribute to the understanding of their mechanical properties and can guide further improvements in material design and processing.
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by jsendak | Jan 1, 2024 | Computer Science
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
Compared to editing single-modal LLMs, multimodal model editing is more
challenging, which demands a higher level of scrutiny and careful consideration
in the editing process. To facilitate research in this area, we construct a new
benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite
of innovative metrics for evaluation. We conduct comprehensive experiments
involving various model editing baselines and analyze the impact of editing
different components for multimodal LLMs. Empirically, we notice that previous
baselines can implement editing multimodal LLMs to some extent, but the effect
is still barely satisfactory, indicating the potential difficulty of this task.
We hope that our work can provide the NLP community with insights. Code and
dataset are available in https://github.com/zjunlp/EasyEdit.
Multimodal Large Language Models (MLLMs) and the Challenges of Editing
In recent years, Multimodal Large Language Models (MLLMs) have garnered significant attention in the field of multimedia information systems. These models, which integrate multiple modalities such as text, images, and even audio, have shown great promise in various applications, including text generation, image captioning, and visual question answering. However, one of the critical challenges associated with MLLMs is editing.
The process of editing multimodal models is far more complex compared to single-modal models. It demands a higher level of scrutiny and careful consideration. This complexity arises due to the need to ensure coherence across different modalities while preserving semantic meaning and maintaining the desired style. For instance, if we want to edit a text generated by an MLLM to change the image content it describes, we must ensure that the modified text remains coherent and aligns with the new image.
Introducing MMEdit: A Benchmark for Editing Multimodal LLMs
To facilitate research in the area of editing multimodal LLMs, the authors of this paper have constructed a new benchmark called MMEdit. This benchmark provides a standardized evaluation framework for testing the effectiveness of various editing techniques and algorithms. By establishing this benchmark, researchers can objectively compare different approaches and measure their performance.
Furthermore, the authors have also introduced a suite of innovative metrics specifically tailored to evaluate the quality of edited multimodal LLMs. These metrics take into account various factors including semantic coherence, style preservation, and alignment between different modalities. This comprehensive evaluation framework will enable researchers to gain deeper insights into the strengths and limitations of different editing techniques.
The Impact of Editing Different Components and Baselines
To analyze the impact of editing different components of multimodal LLMs, the authors conduct comprehensive experiments. They compare the performance of various editing baselines and measure their effectiveness in achieving the desired edits. The results indicate that while previous baselines can achieve some level of editing in multimodal models, the overall effect is still unsatisfactory.
This finding highlights the potential difficulty of the task at hand. It emphasizes the need for further research and development to improve the quality of edited multimodal LLMs. The findings also suggest that existing editing techniques may need to be enhanced or new approaches need to be devised to address the unique challenges posed by these models.
The Wider Field of Multimedia Information Systems and its Connection to AR, VR, and Animation
This paper on editing multimodal LLMs has significant implications for the wider field of multimedia information systems. As we continue to develop advanced technologies such as Augmented Reality (AR), Virtual Reality (VR), and animations, the integration of different modalities, including text and images, becomes crucial. The ability to edit multimodal LLMs effectively can enhance the quality and realism of AR and VR experiences, improve interactive animations, and enable more immersive storytelling.
By focusing on the challenges and techniques associated with editing multimodal LLMs, this research contributes to the advancement of AR, VR, and animation technologies. It lays the groundwork for developing more sophisticated tools and algorithms that can seamlessly edit multimodal content in these domains. This multidisciplinary nature of the research highlights the intersection between natural language processing, multimedia information systems, AR, VR, and animation, emphasizing the need for collaboration between experts from different fields.
In conclusion, the construction of the MMEdit benchmark, the analysis of editing baselines, and the identification of the challenges in editing multimodal LLMs provide significant insights for the NLP community and the wider field of multimedia information systems. This work sets the stage for future research endeavors to tackle the complexity of editing multimodal models and drive innovations in AR, VR, and animation.
Code and dataset for this research can be found at https://github.com/zjunlp/EasyEdit.
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