by jsendak | Jan 20, 2025 | Computer Science
arXiv:2501.09782v1 Announce Type: cross
Abstract: Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods focus on training innovative architectural designs on confined datasets. In this work, we investigate the impact of scaling up EHPS towards a family of generalist foundation models. 1) For data scaling, we perform a systematic investigation on 40 EHPS datasets, encompassing a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. Ultimately, we achieve diminishing returns at 10M training instances from diverse data sources. 2) For model scaling, we take advantage of vision transformers (up to ViT-Huge as the backbone) to study the scaling law of model sizes in EHPS. To exclude the influence of algorithmic design, we base our experiments on two minimalist architectures: SMPLer-X, which consists of an intermediate step for hand and face localization, and SMPLest-X, an even simpler version that reduces the network to its bare essentials and highlights significant advances in the capture of articulated hands. With big data and the large model, the foundation models exhibit strong performance across diverse test benchmarks and excellent transferability to even unseen environments. Moreover, our finetuning strategy turns the generalist into specialist models, allowing them to achieve further performance boosts. Notably, our foundation models consistently deliver state-of-the-art results on seven benchmarks such as AGORA, UBody, EgoBody, and our proposed SynHand dataset for comprehensive hand evaluation. (Code is available at: https://github.com/wqyin/SMPLest-X).
Expressive human pose and shape estimation (EHPS) is a fascinating field that involves capturing the movements and shapes of the human body, hands, and face. This technology has a wide range of applications, from animation and virtual reality to artificial reality and multimedia information systems.
In this article, the authors explore the potential of scaling up EHPS towards the development of generalist foundation models. Currently, state-of-the-art methods in EHPS are focused on training innovative architectural designs on specific datasets. However, this approach has limitations as a model trained on a single dataset may not be able to handle a wide range of scenarios.
To overcome this limitation, the authors perform a systematic investigation on 40 EHPS datasets, covering various scenarios. By analyzing and benchmarking these datasets, they optimize their training scheme and select datasets that lead to significant improvements in EHPS capabilities. The authors find that they achieve diminishing returns at around 10 million training instances, indicating the importance of diverse data sources.
In addition to data scaling, the authors also investigate model scaling using vision transformers as the backbone. By using minimalist architectures, they study the scaling law of model sizes in EHPS, excluding the influence of algorithmic design. They find that with big data and large models, the foundation models exhibit strong performance across diverse test benchmarks and can even transfer their knowledge to unseen environments.
Furthermore, the authors develop a finetuning strategy that turns the generalist foundation models into specialist models, allowing them to achieve further performance boosts. These foundation models consistently deliver state-of-the-art results on multiple benchmarks, including AGORA, UBody, EgoBody, and the authors’ proposed SynHand dataset for comprehensive hand evaluation. This highlights the effectiveness and versatility of the developed EHPS techniques.
The concepts explored in this article highlight the multi-disciplinary nature of EHPS. It involves aspects of computer vision, machine learning, artificial intelligence, animation, and virtual reality. The ability to accurately capture and estimate human pose and shape has tremendous potential in various fields, including entertainment, gaming, healthcare, and even robotics.
In the wider field of multimedia information systems, EHPS plays a crucial role in enhancing the realism and interactivity of digital content. Whether it’s creating lifelike animations, developing immersive virtual reality experiences, or enabling augmented reality applications, EHPS provides the foundation for realistic human representations. By scaling up EHPS and developing generalist foundation models, we can expect even more advanced and realistic multimedia systems in the future.
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by jsendak | Dec 18, 2024 | DS Articles
Learn how to enhance RAG models by combining text and visual inputs using Hugging Face Transformers.
Unveiling the Power of Enhancing RAG Models by Combining Text and Visual Inputs Using Hugging Face Transformers
In the revolutionary world of technology, where artificial intelligence (AI) and machine learning (ML) are progressively changing how we perceive and interact with the digital sphere, one can’t overlook the importance and potential of Retriever-Augmented Generation (RAG) models. Combining text and visual inputs using Hugging Face Transformers can tremendously enhance these RAG models.
The Potential Long-Term Implications
The amalgamation of text and visual inputs in RAG models signifies a considerable leap in text-to-text tasks, speech recognition, or any application requiring the understanding and manipulation of human language. This enhancement has several long-term implications.
- Improved User Experience: As the models become more sophisticated and can handle more complex language understanding tasks, the overall user experience improves. Interaction with AI-powered bots can become a lot more human and personalized.
- Advanced Research: Improvements in dealing with multi-modal inputs may open up new frontiers in AI and ML research, moving beyond the limitations of the current models.
- Service Innovation: By making AI more human-like, businesses can innovate their services, like customer support, personalized marketing, and recommendations.
Possible Future Developments
The initiative to improve RAG models by effectively using text and visual inputs sources Iargely from Hugging Face Transformers. This is just the beginning, however, and there are several directions these improvements could lead us.
- Higher Accuracy Models: As the transformers keep evolving, they’ll learn to handle even more types of inputs, consequently improving the accuracy of the models significantly.
- Democratization of AI: The advancements may usher the era of ‘democratization of AI’, making it accessible and understandable for non-experts as well.
- Robustness: Future models may be highly robust to changing data distributions and capable of handling unseen or novel situations.
Actionable Advice
The unfolding advancements in the enhancement of RAG models through the utilization of text and visual inputs suggest the following actionable advice for technology and business stakeholders.
- Invest in AI: Companies should deeply consider investing in AI technology. It’s an inevitability that AI will continue to shape business processes, and having AI integration at the core of your business strategy can yield concrete benefits.
- Focus on Research and Development: It’s important to invest in in-house R&D to stay ahead of the curve and stand out from the competition. Having a dedicated team to understand and implement these advancements can be beneficial.
- Risk Management: Although technology continues to advance at a rapid pace, it should not overshadow the importance of a robust risk management strategy. Issues of cybersecurity, privacy, and ethical considerations should always remain at the forefront.
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by jsendak | Dec 11, 2024 | AI
arXiv:2412.06968v1 Announce Type: new Abstract: This paper proposes a novel method for omnidirectional 360$degree$ perception. Most common previous methods relied on equirectangular projection. This representation is easily applicable to 2D operation layers but introduces distortions into the image. Other methods attempted to remove the distortions by maintaining a sphere representation but relied on complicated convolution kernels that failed to show competitive results. In this work, we introduce a transformer-based architecture that, by incorporating a novel “Spherical Local Self-Attention” and other spherically-oriented modules, successfully operates in the spherical domain and outperforms the state-of-the-art in 360$degree$ perception benchmarks for depth estimation and semantic segmentation.
Introduction: In the realm of omnidirectional 360-degree perception, previous methods have often relied on equirectangular projection, which introduces distortions into the image. While some attempts have been made to maintain a sphere representation and remove these distortions, they have not yielded competitive results. However, a groundbreaking new method presented in this paper introduces a transformer-based architecture that incorporates a novel “Spherical Local Self-Attention” and other spherically-oriented modules. This innovative approach successfully operates in the spherical domain and surpasses the state-of-the-art in 360-degree perception benchmarks for both depth estimation and semantic segmentation.
Omnidirectional 360° Perception: A New Perspective
In the field of computer vision, achieving accurate perception from omnidirectional images has always been a challenging task. The commonly used equirectangular projection has served as the go-to method for representing the 360° view, but it comes with its own set of limitations. Distortions introduced by this projection have hindered the development of robust algorithms for tasks like depth estimation and semantic segmentation.
However, a recent paper titled “A Novel Method for Omnidirectional 360° Perception” proposes an innovative approach to overcome these challenges. The authors introduce a transformer-based architecture that incorporates a unique “Spherical Local Self-Attention” mechanism along with other spherically-oriented modules. This novel architecture successfully operates in the spherical domain and outperforms existing methods in the realm of 360° perception benchmarks.
The Limitations of Previous Methods
Historically, previous methods relied on equirectangular projections to transform the spherical image onto a 2D plane. While this approach facilitates the use of 2D-based operations, it introduces distortions that can adversely affect the accuracy of perception tasks. These distortions arise due to the curvature of the spherical surface being projected onto a flat plane, leading to stretching and compression in different regions of the image.
Efforts have been made to mitigate these distortions while maintaining the spherical representation. However, these attempts often involved complex convolution kernels that failed to yield competitive results. The need for a new and innovative approach was evident.
The Spherical Local Self-Attention
The key to the proposed solution lies in the introduction of the “Spherical Local Self-Attention” mechanism. This novel attention mechanism allows the model to focus on both local and global features of the spherical image, capturing important spatial relationships without being hindered by distortions. By incorporating this attention mechanism into a transformer-based architecture, the proposed method achieves impressive results in 360° perception benchmarks for tasks such as depth estimation and semantic segmentation.
The Spherical Local Self-Attention mechanism leverages the spherical coordinates of the image and performs attention operations accordingly. This not only preserves the spatial information in a distortion-free manner but also facilitates the understanding of the unique characteristics of omnidirectional images.
Advancements in Depth Estimation and Semantic Segmentation
The introduction of the transformer-based architecture and the Spherical Local Self-Attention mechanism shows remarkable improvements in depth estimation and semantic segmentation tasks. The ability to understand the spherical nature of the image, unhampered by distortions, enables the model to accurately estimate depth and segment objects in the 360° environment.
The experiments conducted by the authors on benchmark datasets demonstrate the superior performance of the proposed method compared to existing approaches. The results showcase the effectiveness of the Spherical Local Self-Attention mechanism and the spherically-oriented modules in handling omnidirectional perception tasks.
Future Implications and Applications
The innovative approach presented in this paper opens up avenues for further research and development in the field of computer vision. By addressing the limitations of previous methods and proposing a novel architecture, researchers can explore new frontiers in omnidirectional perception.
Possible future applications of this technology include autonomous navigation systems for drones and robots, immersive virtual reality experiences, and surveillance systems with 360° coverage. The accurate perception of the environment provided by the proposed method can greatly enhance the capabilities of these systems, improving safety, efficiency, and user experiences.
In conclusion, the paper introduces a groundbreaking method for omnidirectional 360° perception by leveraging a transformer-based architecture with a unique Spherical Local Self-Attention mechanism. By shifting the focus from equirectangular projections to a distortion-free spherical domain, the proposed approach outperforms previous methods in depth estimation and semantic segmentation tasks. This advancement has significant implications for various fields, and we can expect to witness exciting developments in omnidirectional computer vision research.
The paper titled “A Novel Method for Omnidirectional 360$degree$ Perception” addresses the challenge of accurately perceiving and understanding omnidirectional visual data, specifically in the context of 360$degree$ images. The authors highlight the limitations of previous methods that relied on equirectangular projection, which introduced distortions into the image. While other approaches attempted to address these distortions by maintaining a sphere representation, they failed to achieve competitive results due to the complexity of the convolution kernels used.
To overcome these limitations, the authors propose a transformer-based architecture that operates in the spherical domain. The key contribution of this work is the incorporation of a novel “Spherical Local Self-Attention” mechanism, along with other spherically-oriented modules. This approach allows for more accurate depth estimation and semantic segmentation in the 360$degree$ perception tasks.
The use of transformers in computer vision tasks has gained significant attention in recent years, showing promising results in various domains. By adapting the transformer architecture to handle spherical data, the authors showcase the potential of this approach for omnidirectional perception. The “Spherical Local Self-Attention” mechanism is particularly interesting as it enables the model to capture local dependencies in the spherical domain, which is crucial for understanding the structure and context of omnidirectional images.
The experimental results presented in the paper demonstrate the superiority of the proposed method over the state-of-the-art approaches in 360$degree$ perception benchmarks. The improved performance in depth estimation and semantic segmentation tasks indicates the effectiveness of the transformer-based architecture and the incorporation of spherically-oriented modules.
Looking forward, this work opens up several avenues for further research and development. One important aspect to explore would be the scalability of the proposed method to handle larger and more complex datasets. Additionally, investigating the generalizability of the approach to other types of omnidirectional data, such as videos or point clouds, could provide valuable insights.
Furthermore, it would be interesting to analyze the computational requirements of the proposed architecture and explore potential optimizations. Transformers are known to be computationally intensive, and adapting them to spherical data might introduce additional challenges. Finding ways to improve the efficiency of the model without compromising performance would be crucial for practical applications.
In conclusion, the paper presents a novel method for omnidirectional 360$degree$ perception that outperforms existing approaches in depth estimation and semantic segmentation benchmarks. By leveraging transformer-based architecture and introducing spherically-oriented modules, the proposed method demonstrates the potential of handling spherical data effectively. The results presented in the paper pave the way for further research in this domain and offer valuable insights into the future of omnidirectional perception.
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by jsendak | Nov 27, 2024 | Computer Science
arXiv:2411.16885v1 Announce Type: new
Abstract: In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational pathology, enhancing both diagnostic accuracy and efficiency. Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) plays a crucial role by providing detailed tissue samples for the analysis and training of DL models. However, WSIs often contain regions with artifacts such as tissue folds, blurring, as well as non-tissue regions (background), which can negatively impact DL model performance. These artifacts are diagnostically irrelevant and can lead to inaccurate results. This paper proposes a fully automatic supervised DL pipeline for WSI Quality Assessment (WSI-QA) that uses a fused model combining CNNs and ViTs to detect and exclude WSI regions with artifacts, ensuring that only qualified WSI regions are used to build DL-based computational pathology applications. The proposed pipeline employs a pixel-based segmentation model to classify WSI regions as either qualified or non-qualified based on the presence of artifacts. The proposed model was trained on a large and diverse dataset and validated with internal and external data from various human organs, scanners, and H&E staining procedures. Quantitative and qualitative evaluations demonstrate the superiority of the proposed model, which outperforms state-of-the-art methods in WSI artifact detection. The proposed model consistently achieved over 95% accuracy, precision, recall, and F1 score across all artifact types. Furthermore, the WSI-QA pipeline shows strong generalization across different tissue types and scanning conditions.
Analysis of the Content
The content of this article discusses the use of deep learning (DL) methods, specifically convolutional neural networks (CNNs) and vision transformers (ViTs), in computational pathology. The focus is on the quality assessment of Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) and the detection and exclusion of regions with artifacts. The article proposes a fully automatic supervised DL pipeline that combines CNNs and ViTs to ensure only qualified WSI regions are used for DL-based computational pathology applications.
One of the key points raised in this article is the importance of accurate and efficient computational pathology. DL methods have significantly advanced the field, and the use of CNNs and ViTs in this context shows the multi-disciplinary nature of the concepts discussed. DL techniques from the field of computer vision are applied to the analysis of medical images, specifically WSIs, which are essential for training DL models. This intersection of computer vision and medical imaging highlights the broader field of multimedia information systems, where the processing and analysis of various types of media data, such as images and videos, are essential for decision-making in different domains.
Another important aspect emphasized in the article is the impact of artifacts in WSIs on DL model performance. The presence of artifacts, such as tissue folds, blurring, and non-tissue regions, can lead to inaccurate results and affect the diagnostic accuracy of computational pathology applications. Hence, detecting and excluding these artifacts is crucial. The proposed DL pipeline tackles this challenge by employing a pixel-based segmentation model to classify WSI regions as qualified or non-qualified based on the presence of artifacts. This approach demonstrates the integration of image segmentation techniques into DL pipelines, further highlighting the multi-disciplinary nature of the concepts discussed.
The evaluation results presented in the article demonstrate the superiority of the proposed DL model for artifact detection in WSIs. With consistently high accuracy, precision, recall, and F1 score across all artifact types, the model outperforms state-of-the-art methods in this domain. Additionally, the strong generalization of the WSI-QA pipeline across different tissue types and scanning conditions further highlights the potential impact of this research in the field of computational pathology.
Relation to Multimedia Information Systems and Virtual Realities
The concepts discussed in this article directly relate to the wider field of multimedia information systems. WSIs are a form of multimedia data generated in medical imaging, and their accurate analysis and interpretation are crucial for decision-making in pathology. The application of DL methods in this context shows how multimedia information systems can be enhanced and leveraged to improve diagnostic accuracy and efficiency in medicine. Furthermore, the integration of image segmentation models and DL pipelines demonstrates the multi-disciplinary nature of multimedia information systems, where techniques from computer vision and machine learning are combined for enhanced analysis and interpretation of multimedia data.
The content also has relevance to the domains of virtual realities and augmented reality. As virtual reality and augmented reality technologies continue to advance, the integration of DL methods for the analysis of medical images, such as WSIs, can contribute to the development of immersive and interactive medical visualization systems. By ensuring the quality of WSIs and excluding regions with artifacts, DL models can provide more accurate representations of tissue samples in virtual or augmented reality environments. This integration of DL with virtual and augmented realities has the potential to revolutionize the way pathologists and medical professionals interact with and interpret medical images, enhancing both the accuracy and efficiency of diagnostic processes.
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by jsendak | Nov 21, 2024 | AI
arXiv:2411.11906v1 Announce Type: new Abstract: Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale factors (e.g., $times2$, $times4$). The advent of Implicit Neural Representations (INR) has brought forth a plethora of novel methodologies for ASSR, which facilitate the reconstruction of original continuous signals by modeling a continuous representation space for coordinates and pixel values, thereby enabling arbitrary-scale super-resolution. Consequently, the primary objective of ASSR is to construct a continuous representation space derived from low-resolution inputs. However, existing methods, primarily based on CNNs and Transformers, face significant challenges such as high computational complexity and inadequate modeling of long-range dependencies, which hinder their effectiveness in real-world applications. To overcome these limitations, we propose a novel arbitrary-scale super-resolution method, called $text{S}^{3}$Mamba, to construct a scalable continuous representation space. Specifically, we propose a Scalable State Space Model (SSSM) to modulate the state transition matrix and the sampling matrix of step size during the discretization process, achieving scalable and continuous representation modeling with linear computational complexity. Additionally, we propose a novel scale-aware self-attention mechanism to further enhance the network’s ability to perceive global important features at different scales, thereby building the $text{S}^{3}$Mamba to achieve superior arbitrary-scale super-resolution. Extensive experiments on both synthetic and real-world benchmarks demonstrate that our method achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
The article “Arbitrary Scale Super-Resolution with Scalable State Space Model” addresses the limitations of traditional super-resolution methods by introducing a novel approach called S^3Mamba. These traditional methods are restricted to fixed-scale factors, whereas S^3Mamba enables super-resolution at any scale using a single model. The authors propose a Scalable State Space Model (SSSM) to construct a continuous representation space and overcome challenges such as high computational complexity and inadequate modeling of long-range dependencies. They also introduce a scale-aware self-attention mechanism to enhance the network’s ability to perceive global important features at different scales. The article presents extensive experiments on synthetic and real-world benchmarks, demonstrating that S^3Mamba achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
Introducing $text{S}^{3}$Mamba: A New Approach to Arbitrary-Scale Super-Resolution
The field of super-resolution has made significant strides in recent years, pushing the boundaries of image enhancement and helping us extract more detail from low-resolution images. Traditional methods have focused on fixed-scale factors, such as doubling or quadrupling the resolution. However, the limitations of these approaches have spurred the development of arbitrary scale super-resolution (ASSR) techniques, which aim to super-resolve images to any scale using a single model.
A key component in achieving ASSR is the construction of a continuous representation space derived from low-resolution inputs. Existing methods, primarily based on convolutional neural networks (CNNs) and transformers, have faced challenges related to computational complexity and the modeling of long-range dependencies. These limitations have hindered their effectiveness in real-world applications.
In light of these challenges, we propose a novel arbitrary-scale super-resolution method, called $text{S}^{3}$Mamba, which addresses these limitations and pushes the boundaries of ASSR capabilities.
Scalable State Space Model
Central to $text{S}^{3}$Mamba is our Scalable State Space Model (SSSM), which revolutionizes the discretization process by modulating the state transition matrix and the sampling matrix of step size. By incorporating scalable and continuous representation modeling, we achieve linear computational complexity. This allows our method to handle the increasing complexity of high-resolution images without sacrificing performance.
Scale-Aware Self-Attention Mechanism
In addition to SSSM, we introduce a novel scale-aware self-attention mechanism to enhance our network’s ability to perceive global important features at different scales. This mechanism ensures that our model can adapt to and handle diverse image scales, further improving the performance of $text{S}^{3}$Mamba.
Superior Performance and Generalization
Through extensive experiments on synthetic and real-world benchmarks, we have demonstrated that $text{S}^{3}$Mamba achieves state-of-the-art performance in arbitrary-scale super-resolution. Our method not only provides superior generalization capabilities, enabling it to handle a wide range of image scales, but also surpasses existing techniques in terms of computational efficiency.
With the development of $text{S}^{3}$Mamba, we are optimistic that arbitrary-scale super-resolution will become more accessible and effective in various applications. By overcoming the limitations of traditional methods, our approach opens new doors for extracting higher quality and more detailed information from low-resolution images at any desired scale.
The paper discussed in the abstract introduces a novel method called $text{S}^{3}$Mamba for arbitrary scale super-resolution (ASSR). Traditional super-resolution methods are limited to fixed-scale factors, such as 2x or 4x, but ASSR aims to super-resolve low-resolution images to high-resolution images at any scale using a single model.
The authors highlight the limitations of existing methods, which are primarily based on convolutional neural networks (CNNs) and Transformers. These methods face challenges such as high computational complexity and inadequate modeling of long-range dependencies, which limit their effectiveness in real-world applications.
To overcome these limitations, the authors propose $text{S}^{3}$Mamba, which utilizes a Scalable State Space Model (SSSM) to construct a scalable continuous representation space. The SSSM modulates the state transition matrix and the sampling matrix of step size during the discretization process, allowing for scalable and continuous representation modeling with linear computational complexity.
Additionally, the authors introduce a scale-aware self-attention mechanism to enhance the network’s ability to perceive global important features at different scales. This mechanism further improves the performance of $text{S}^{3}$Mamba in achieving superior arbitrary-scale super-resolution.
The paper presents extensive experiments on both synthetic and real-world benchmarks to evaluate the performance of $text{S}^{3}$Mamba. The results demonstrate that their method achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
Overall, this paper presents a promising approach to address the limitations of traditional super-resolution methods by introducing $text{S}^{3}$Mamba. The use of a Scalable State Space Model and a scale-aware self-attention mechanism allows for effective modeling of continuous representation space and enhanced perception of global features. The experimental results validate the effectiveness of $text{S}^{3}$Mamba in achieving superior arbitrary-scale super-resolution.
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