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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by jsendak | Jan 8, 2024 | Computer Science
AI/ML-Based Direct Positioning: A Comprehensive Review
Direct positioning within 5G systems has recently gained significant attention due to its potential in overcoming the limitations of conventional methods in challenging scenarios and conditions. In this comprehensive review, we delve into the insights provided by the technical report TR38.843 and explore the various aspects associated with Life Cycle Management (LCM) in the direct positioning process.
Challenging Scenarios and Conditions
One of the key advantages of AI/ML-based direct positioning is its ability to perform accurately even in demanding scenarios where conventional methods often fall short. These challenging conditions may include dense urban environments with high-rise buildings, indoor settings where signals are greatly attenuated, or rural areas with a limited number of base stations.
The technical report TR38.843 sheds light on these scenarios, providing simulation results and key observations that demonstrate the effectiveness of AI/ML algorithms in direct positioning. The utilization of advanced machine learning techniques allows for improved accuracy, reliability, and robustness even in the most challenging situations.
Life Cycle Management (LCM)
Within the direct positioning process, Life Cycle Management plays a crucial role in ensuring the efficient operation of AI/ML algorithms. This includes stages such as model training, validation, deployment, and adaptation. TR38.843 highlights the importance of each LCM aspect and provides guidelines for optimizing them.
Measurement Reporting: Accurate and timely reporting of measurement data is vital for training AI models. The technical report emphasizes the need for standardized reporting formats and protocols to ensure compatibility across different network infrastructures, enabling seamless integration of AI/ML algorithms.
Data Collection: As the saying goes, “Garbage in, garbage out.” High-quality data is essential for reliable direct positioning. TR38.843 discusses the challenges associated with data collection, such as privacy concerns, and proposes solutions like federated learning and differential privacy to address these issues.
Model Management: Managing AI/ML models effectively is crucial for their continuous improvement and adaptation. This involves version control, model repository management, and monitoring model performance in real-time. TR38.843 highlights best practices for model management, including the use of cloud-based platforms and automated processes.
Advancing Direct Positioning
Selected solutions discussed in the technical report have the potential to significantly advance direct positioning in 5G systems. These solutions aim to improve accuracy, reliability, and efficiency in the direct positioning process.
By addressing measurement reporting issues, such as standardization and compatibility, AI/ML-based direct positioning can be seamlessly integrated into existing network infrastructures. This will unlock new possibilities for location-based services and applications that rely on precise positioning information.
The proposed data collection solutions, such as federated learning and differential privacy, not only address privacy concerns but also enable the utilization of vast amounts of data from different sources. This data diversity enhances the performance of AI/ML algorithms, leading to more reliable direct positioning results.
Effective model management practices outlined in TR38.843 ensure that AI/ML models remain up-to-date and adaptable to changing conditions. Cloud-based platforms and automated processes simplify the management workflow, enabling continuous improvement of direct posi
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by jsendak | Jan 8, 2024 | Computer Science
In addressing the challenge of interpretability and generalizability of
artificial music intelligence, this paper introduces a novel symbolic
representation that amalgamates both explicit and implicit musical information
across diverse traditions and granularities. Utilizing a hierarchical and-or
graph representation, the model employs nodes and edges to encapsulate a broad
spectrum of musical elements, including structures, textures, rhythms, and
harmonies. This hierarchical approach expands the representability across
various scales of music. This representation serves as the foundation for an
energy-based model, uniquely tailored to learn musical concepts through a
flexible algorithm framework relying on the minimax entropy principle.
Utilizing an adapted Metropolis-Hastings sampling technique, the model enables
fine-grained control over music generation. A comprehensive empirical
evaluation, contrasting this novel approach with existing methodologies,
manifests considerable advancements in interpretability and controllability.
This study marks a substantial contribution to the fields of music analysis,
composition, and computational musicology.
Enhancing Interpretability and Generalizability in Artificial Music Intelligence
Artificial music intelligence is a rapidly developing field that combines various disciplines such as computer science, musicology, and cognitive science. The challenge of interpretability and generalizability in this domain has always been a complex issue. However, this new research paper introduces a novel symbolic representation that aims to address these challenges.
Hierarchical and-or Graph Representation
The paper proposes a hierarchical and-or graph representation model that encompasses both explicit and implicit musical information from diverse traditions and granularities. This approach allows for the encapsulation of various musical elements, including structures, textures, rhythms, and harmonies. By utilizing nodes and edges, the model represents a wide range of musical concepts and their relationships.
This multi-disciplinary approach is of great importance in the wider field of multimedia information systems. It enables the integration of different elements such as audio, visuals, and interactive interfaces to create immersive experiences for users. By incorporating hierarchical structures and graph representation, this model can support the creation of complex multimedia systems that enhance user engagement.
Energy-Based Model and Minimax Entropy Principle
In order to learn musical concepts and generate music, the paper proposes an energy-based model. This model utilizes an adapted Metropolis-Hastings sampling technique along with a flexible algorithm framework based on the minimax entropy principle.
This approach is closely related to the fields of animations, artificial reality, augmented reality, and virtual realities. Generating music with fine-grained control requires the integration of various multimedia elements such as visuals, animations, and virtual environments. By leveraging energy-based models and entropy principles, it becomes possible to create dynamic and interactive music experiences that adapt to users’ inputs and preferences.
Advancements in Interpretability and Controllability
The comprehensive empirical evaluation presented in the paper demonstrates significant advancements in the interpretability and controllability of the proposed approach compared to existing methodologies. This is a crucial development, considering the inherent complexity of music and the challenge of translating it into AI models.
Furthermore, this study contributes to the fields of music analysis, composition, and computational musicology. By providing a robust foundation for understanding and generating music, this research opens up new avenues for exploration in these disciplines. Researchers and practitioners can leverage this novel approach to create innovative musical compositions and gain deeper insights into the complexities of music.
In conclusion, the introduction of a hierarchical and-or graph representation coupled with an energy-based model and minimax entropy principle marks a significant advancement in the field of artificial music intelligence. The multi-disciplinary nature of the concepts explored in this paper connects it to wider fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. As further research builds upon these foundations, we can anticipate even more exciting developments in the realm of AI-generated music.
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by jsendak | Jan 8, 2024 | Computer Science
Abstract:
The ‘Problematic Paper Screener’ (PPS, WCRI’22, this URL) flagged 12k+ questionable articles featuring tortured phrases, such as ‘glucose bigotry’ instead of ‘glucose intolerance.’ It daily screens the literature for ‘fingerprints’ from a list of 4k tortured phrases known to reflect nonsensical paraphrasing with synonyms. We identified a concentration of ‘tortured articles’ in IEEE conferences and reported our concerns in November 2022 (this URL). This WCRI submission unveils ‘tortured conference series’: questionable articles that keep being accepted in successive conference editions.
Expert Commentary
The identification of problematic academic papers is an ongoing concern in the research community. The ‘Problematic Paper Screener’ (PPS) described in this article is a valuable tool in detecting articles that feature tortured phrases, which can indicate nonsensical paraphrasing with synonyms. By daily screening the literature for ‘fingerprints’ from a curated list of 4,000 such phrases, the PPS has successfully flagged over 12,000 questionable articles.
One particularly notable finding of this study is the concentration of these “tortured articles” in IEEE conferences. The Institute of Electrical and Electronics Engineers (IEEE) is a prestigious organization that hosts numerous conferences in various fields of engineering and technology. The fact that a significant number of questionable articles have been identified in IEEE conferences raises concerns about the quality control and review processes within these conferences.
The authors also mention that they reported their concerns regarding these problematic articles to the relevant parties in November 2022. It would be interesting to learn more about the response and actions taken by IEEE and other conference organizers to address this issue. Transparency and accountability in the academic publishing process are crucial for maintaining the integrity and credibility of research.
Furthermore, this submission to WCRI reveals another alarming trend – the acceptance of these questionable articles in successive conference editions. This pattern suggests a systemic issue in the review and selection process, as these papers seem to pass through multiple rounds of evaluation without being properly vetted. Conference organizers should thoroughly investigate these findings and take appropriate measures to ensure that only high-quality and rigorously reviewed papers are included in future conference proceedings.
Moving forward, it would be beneficial for the research community to adopt similar screening tools like the PPS to identify and mitigate the presence of problematic articles. Additionally, conference organizers must review and strengthen their review processes, including enhanced scrutiny of submitted papers, improved quality control measures, and potentially implementing an appeals system for researchers who believe their work has been unfairly rejected or overlooked.
In conclusion, this article sheds light on the issue of problematic academic papers and emphasizes the need for robust quality control mechanisms both at the individual article level and within conference proceedings. It is reassuring to see researchers actively identifying and addressing these concerns, but ongoing efforts are required to maintain the credibility and reliability of scholarly publications.
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by jsendak | Jan 8, 2024 | Computer Science
Quality assessment of images and videos emphasizes both local details and
global semantics, whereas general data sampling methods (e.g., resizing,
cropping or grid-based fragment) fail to catch them simultaneously. To address
the deficiency, current approaches have to adopt multi-branch models and take
as input the multi-resolution data, which burdens the model complexity. In this
work, instead of stacking up models, a more elegant data sampling method (named
as SAMA, scaling and masking) is explored, which compacts both the local and
global content in a regular input size. The basic idea is to scale the data
into a pyramid first, and reduce the pyramid into a regular data dimension with
a masking strategy. Benefiting from the spatial and temporal redundancy in
images and videos, the processed data maintains the multi-scale characteristics
with a regular input size, thus can be processed by a single-branch model. We
verify the sampling method in image and video quality assessment. Experiments
show that our sampling method can improve the performance of current
single-branch models significantly, and achieves competitive performance to the
multi-branch models without extra model complexity. The source code will be
available at https://github.com/Sissuire/SAMA.
The Multi-disciplinary Nature of Multimedia Information Systems
Quality assessment of images and videos is a crucial task in the field of multimedia information systems. It requires a deep understanding of both the local details and global semantics of the content. However, traditional data sampling methods like resizing, cropping, or grid-based fragments often fail to capture these aspects simultaneously.
In recent years, there has been a growing trend in adopting multi-branch models to address this deficiency. These models take as input multi-resolution data to capture both local and global content. While effective, this approach leads to increased model complexity and resource requirements.
This article introduces a novel data sampling method called SAMA (Scaling and Masking) that offers a more elegant solution to the problem. SAMA aims to compact both local and global content in a regular input size, without the need for stacking up models.
The underlying idea of SAMA is to first scale the data into a pyramid structure. By reducing this pyramid into a regular data dimension using masking strategies, the processed data maintains its multi-scale characteristics. This compacted data can then be efficiently processed by a single-branch model.
One of the key advantages of SAMA is its ability to leverage the spatial and temporal redundancy present in images and videos. This redundancy allows SAMA to maintain the multi-scale characteristics while reducing the input size, resulting in improved performance without adding extra model complexity.
Relation to Animation, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article have significant relevance to the wider field of Animation, Artificial Reality, Augmented Reality, and Virtual Realities. These fields heavily rely on multimedia content, including images and videos.
In Animation, the quality assessment of visual elements is crucial for creating realistic and immersive environments. By applying the SAMA method to assess the quality of animation frames, animators can ensure that the local details and global semantics are accurately captured, leading to a more authentic animated experience.
Artificial Reality, Augmented Reality, and Virtual Realities often involve the integration of virtual and real-world content. Quality assessment becomes essential when merging these elements seamlessly. SAMA can be instrumental in analyzing and comparing the quality of virtual and real-world images and videos, ensuring that the user experiences a smooth transition without perceptible differences.
Moreover, the multi-disciplinary nature of multimedia information systems allows the adoption of SAMA in a wide range of applications. From video surveillance systems to image recognition algorithms, SAMA’s ability to improve the performance of single-branch models can be harnessed across various domains.
In conclusion, the introduction of SAMA as a data sampling method offers a promising approach to quality assessment in multimedia information systems. Its effectiveness in capturing both local details and global semantics without increasing model complexity makes it a valuable addition to the field. As technology continues to advance, the applications of SAMA in Animation, Artificial Reality, Augmented Reality, and Virtual Realities will undoubtedly expand, leading to enhanced user experiences and improved multimedia content.
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