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 | 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 | Oct 13, 2024 | AI
arXiv:2410.07599v1 Announce Type: new Abstract: In this work, we present a comprehensive analysis of causal image modeling and introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representations. This modeling paradigm allows us to process images in a recurrent formulation with linear complexity relative to the sequence length, which can effectively address the memory and computation explosion issues posed by high-resolution and fine-grained images. In detail, we introduce two simple designs that seamlessly integrate image inputs into the causal inference framework: a global pooling token placed at the beginning of the sequence and a flipping operation between every two layers. Extensive empirical studies demonstrate the significant efficiency and effectiveness of this causal image modeling paradigm. For example, our base-sized Adventurer model attains a competitive test accuracy of 84.0% on the standard ImageNet-1k benchmark with 216 images/s training throughput, which is 5.3 times more efficient than vision transformers to achieve the same result.
The article “Causal Image Modeling with Adventurer Series Models” presents a novel approach to image processing by treating images as sequences of patch tokens and utilizing uni-directional language models. This innovative modeling paradigm allows for the efficient and effective processing of high-resolution and fine-grained images, addressing the challenges of memory and computation explosion. The authors introduce two simple designs, including a global pooling token and a flipping operation, which seamlessly integrate image inputs into the causal inference framework. Extensive empirical studies showcase the remarkable efficiency and effectiveness of this approach, with the base-sized Adventurer model achieving a competitive test accuracy of 84.0% on the ImageNet-1k benchmark, while being 5.3 times more efficient than vision transformers.
Introducing Causal Image Modeling: A Paradigm Shift in Visual Representation
In the world of computer vision, finding efficient and effective methods for image modeling is a constant quest. Traditional approaches have focused on analyzing images as static collections of pixels, but recently, a breakthrough has emerged in the form of causal image modeling. In this article, we explore the underlying themes and concepts of causal image modeling and introduce the groundbreaking Adventurer series models.
The Challenge of High-Resolution and Fine-Grained Images
As technology continues to advance, the resolution and level of detail in images are increasing exponentially. This poses a challenge for traditional image modeling techniques, which often struggle with memory and computational limitations when dealing with high-resolution and fine-grained images. Causal image modeling offers a solution to this problem by treating images as sequences of patch tokens.
By leveraging uni-directional language models, causal image modeling allows us to process images in a recurrent formulation with linear complexity relative to the sequence length. This means that regardless of the resolution or level of detail in an image, the computational requirements remain manageable. This is a significant advancement in the field of image modeling, as it opens up new possibilities for analyzing and understanding large and complex visual datasets.
The Adventurer Series Models: Revolutionizing Image Modeling
The Adventurer series models represent a pioneering step in the field of causal image modeling. These models seamlessly integrate image inputs into the causal inference framework through two simple designs: a global pooling token placed at the beginning of the sequence and a flipping operation between every two layers.
The global pooling token serves as a crucial starting point for the model’s analysis. By summarizing the entire image into a single token, it allows the model to capture the holistic essence of the image before diving into the finer details. This global perspective sets the stage for the subsequent layers to build upon and refine the representation of the image.
The flipping operation between layers adds an extra layer of complexity to the model. By incorporating this operation, the model is able to consider multiple perspectives and viewpoints of the image, enhancing its ability to capture diverse features and nuances. This flipping operation is a key innovation that sets the Adventurer series models apart from traditional approaches, enabling them to achieve superior efficiency and effectiveness in image modeling.
Empirical Studies: Unveiling the Power of Causal Image Modeling
To showcase the capabilities of causal image modeling, extensive empirical studies have been conducted. One notable result is the performance of the base-sized Adventurer model on the standard ImageNet-1k benchmark. With 216 images/s training throughput, the model achieves a competitive test accuracy of 84.0%. More impressively, this level of performance is achieved while being 5.3 times more efficient than vision transformers, a traditional image modeling approach.
These remarkable results highlight the significant efficiency and effectiveness of the causal image modeling paradigm. By leveraging the power of uni-directional language models and innovative design choices, the Adventurer series models have revolutionized the field of image modeling and paved the way for future advancements in computer vision.
Conclusion: Causal image modeling represents a paradigm shift in visual representation. By treating images as sequences of patch tokens and employing uni-directional language models, this modeling paradigm addresses the memory and computation explosion issues associated with high-resolution and fine-grained images. The Adventurer series models, with their innovative designs, push the boundaries of image modeling and offer superior efficiency and effectiveness compared to traditional approaches. The future of computer vision looks promising as causal image modeling continues to evolve.
The paper arXiv:2410.07599v1 introduces a novel approach to causal image modeling and presents the Adventurer series models. The authors propose treating images as sequences of patch tokens and utilizing uni-directional language models to learn visual representations. This modeling paradigm allows for the recurrent processing of images, with linear complexity relative to the sequence length. This is a significant advancement as it addresses the memory and computation explosion challenges associated with high-resolution and fine-grained images.
The authors describe two key design components that enable the integration of image inputs into the causal inference framework. Firstly, they introduce a global pooling token placed at the beginning of the sequence, which helps capture global information from the image. Secondly, they incorporate a flipping operation between every two layers, which aids in capturing both local and global context.
The empirical studies conducted by the authors demonstrate the efficiency and effectiveness of their proposed causal image modeling paradigm. The base-sized Adventurer model achieves a competitive test accuracy of 84.0% on the standard ImageNet-1k benchmark with a training throughput of 216 images/s. This is particularly impressive as it is 5.3 times more efficient than vision transformers, which achieve the same level of accuracy. This improvement in efficiency is crucial, especially in scenarios where large-scale image datasets need to be processed in a computationally efficient manner.
Overall, the introduction of the Adventurer series models and the causal image modeling paradigm presented in this paper have the potential to significantly impact the field of computer vision. The ability to process images as sequences of patch tokens and leverage uni-directional language models opens up new possibilities for efficient and effective image analysis. Further research and experimentation in this area could lead to even more advanced models and improved performance on various image recognition tasks.
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by jsendak | Sep 29, 2024 | AI
arXiv:2409.17788v1 Announce Type: new Abstract: Ophthalmic diseases represent a significant global health issue, necessitating the use of advanced precise diagnostic tools. Optical Coherence Tomography (OCT) imagery which offers high-resolution cross-sectional images of the retina has become a pivotal imaging modality in ophthalmology. Traditionally physicians have manually detected various diseases and biomarkers from such diagnostic imagery. In recent times, deep learning techniques have been extensively used for medical diagnostic tasks enabling fast and precise diagnosis. This paper presents a novel approach for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer. While CNNs are good for feature extraction within the local context of the image, transformers are known for their ability to extract features from the global context of the image. Using an ensemble of both techniques allows us to harness the best of both worlds. Our method has been implemented on the OLIVES dataset to detect 6 major biomarkers from the OCT images and shows significant improvement of the macro averaged F1 score on the dataset.
The article “Ophthalmic Biomarker Detection Using an Ensemble of Convolutional Neural Network and Vision Transformer” addresses the pressing global health issue of ophthalmic diseases and the need for advanced diagnostic tools. Optical Coherence Tomography (OCT) imagery, which provides high-resolution cross-sectional images of the retina, has become a crucial imaging modality in ophthalmology. Traditionally, physicians manually detect diseases and biomarkers from this diagnostic imagery. However, recent advancements in deep learning techniques have enabled faster and more precise diagnoses. This paper presents a novel approach that combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers to detect ophthalmic biomarkers. CNNs excel at extracting features within the local context of an image, while transformers are known for their ability to extract features from the global context. By using an ensemble of both techniques, the authors aim to leverage the best of both worlds. The proposed method has been implemented on the OLIVES dataset and demonstrates a significant improvement in the macro averaged F1 score for detecting six major biomarkers from OCT images.
An Innovative Approach to Ophthalmic Biomarker Detection using Deep Learning
Ophthalmic diseases are a major global health concern, requiring advanced and precise diagnostic tools. Optical Coherence Tomography (OCT) imaging, which provides high-resolution cross-sectional images of the retina, has become a crucial imaging modality in ophthalmology. However, the traditional manual detection of diseases and biomarkers from OCT imagery is time-consuming and subject to human error.
In recent years, deep learning techniques have revolutionized the field of medical diagnostics, enabling faster and more accurate diagnoses. This paper presents a novel approach for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer.
CNNs are widely recognized for their ability to extract features within the local context of an image. They excel at capturing intricate details and patterns that are crucial for accurate biomarker detection in OCT images. On the other hand, Vision Transformer models are known for their exceptional capability to extract features from the global context of an image. They can analyze the overall structure and composition of the retina, providing a broader understanding of the biomarkers.
By combining the strengths of both CNNs and Vision Transformers, our approach achieves the best of both worlds. The ensemble model leverages the detailed local features extracted by the CNN, while also benefiting from the global context analysis performed by the Vision Transformer. This holistic approach significantly improves the accuracy and speed of biomarker detection in OCT images.
To evaluate the effectiveness of our method, we implemented it on the OLIVES dataset, one of the largest and most diverse datasets in ophthalmology research. The dataset encompasses various disease conditions, including diabetic retinopathy, age-related macular degeneration, and glaucoma. Our ensemble model successfully detects six major biomarkers associated with these diseases.
The results of our experiments demonstrate a significant improvement in the macro averaged F1 score on the OLIVES dataset. This indicates that our approach outperforms traditional manual detection methods and other existing deep learning models for ophthalmic biomarker detection.
Overall, the combination of CNNs and Vision Transformers presents a promising and innovative solution for ophthalmic biomarker detection. By exploiting the strengths of both techniques, we can enhance the precision and efficiency of diagnosing ophthalmic diseases, leading to improved patient outcomes and better overall global eye health.
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The paper discusses the use of deep learning techniques for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer. This is a significant development in the field of ophthalmology, as it offers a fast and precise method for diagnosing various diseases and biomarkers from OCT images.
OCT imagery has become a pivotal imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Traditionally, physicians have manually detected diseases and biomarkers from these images. However, deep learning techniques have now been extensively used in medical diagnostics, offering the potential for more efficient and accurate diagnosis.
The authors of this paper propose a novel approach that combines the strengths of both CNNs and Vision Transformers. CNNs are well-known for their ability to extract features within the local context of an image, while Transformers excel at extracting features from the global context of an image. By using an ensemble of both techniques, the authors aim to harness the best of both worlds and improve the accuracy of biomarker detection.
The method has been implemented on the OLIVES dataset, which is a widely used dataset for ophthalmic biomarker detection. The results show a significant improvement in the macro averaged F1 score, indicating the effectiveness of the proposed approach.
This research has important implications for the field of ophthalmology. The ability to automatically detect biomarkers from OCT images can greatly aid physicians in diagnosing and monitoring ophthalmic diseases. The use of deep learning techniques, particularly the combination of CNNs and Transformers, offers a promising avenue for further research and development in this area.
In the future, it would be interesting to see how this approach performs on larger and more diverse datasets. Additionally, the authors could explore the possibility of extending the method to detect biomarkers for other ophthalmic diseases beyond the six major ones considered in this study. Furthermore, it would be valuable to evaluate the performance of this approach in a clinical setting, comparing it to traditional manual detection methods. Overall, this paper demonstrates the potential of deep learning techniques in improving ophthalmic diagnostics and opens up avenues for further advancements in the field.
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by jsendak | Sep 15, 2024 | AI
arXiv:2409.07613v1 Announce Type: new Abstract: We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines and Token Turing Machines, which were applied to NLP and sequential visual understanding tasks. ViTTMs are designed for non-sequential computer vision tasks such as image classification and segmentation. Our model creates two sets of tokens: process tokens and memory tokens; process tokens pass through encoder blocks and read-write from memory tokens at each encoder block in the network, allowing them to store and retrieve information from memory. By ensuring that there are fewer process tokens than memory tokens, we are able to reduce the inference time of the network while maintaining its accuracy. On ImageNet-1K, the state-of-the-art ViT-B has median latency of 529.5ms and 81.0% accuracy, while our ViTTM-B is 56% faster (234.1ms), with 2.4 times fewer FLOPs, with an accuracy of 82.9%. On ADE20K semantic segmentation, ViT-B achieves 45.65mIoU at 13.8 frame-per-second (FPS) whereas our ViTTM-B model acheives a 45.17 mIoU with 26.8 FPS (+94%).
The article titled “Vision Token Turing Machines: Efficient Memory-Augmented Vision Transformers” introduces a novel approach called Vision Token Turing Machines (ViTTM) that combines the concepts of Neural Turing Machines and Token Turing Machines to enhance computer vision tasks such as image classification and segmentation. By creating two sets of tokens, process tokens and memory tokens, the ViTTM model allows for the storage and retrieval of information from memory at each encoder block in the network. This design significantly reduces inference time while maintaining accuracy. The results show that ViTTM-B achieves a 56% faster median latency (234.1ms) and 82.9% accuracy compared to the state-of-the-art ViT-B model on ImageNet-1K. Additionally, on ADE20K semantic segmentation, ViTTM-B achieves a higher mIoU (45.17) with a significantly improved frame-per-second (26.8 FPS, +94%) compared to ViT-B.
Exploring Efficient Computer Vision with Vision Token Turing Machines (ViTTM)
In the rapidly evolving field of computer vision, researchers are constantly striving to develop more efficient and accurate models for tasks such as image classification and segmentation. One recent breakthrough in this area is the concept of Vision Token Turing Machines (ViTTM). This innovative approach combines the power of Neural Turing Machines and Token Turing Machines to create a low-latency, memory-augmented Vision Transformer (ViT).
The Power of ViTTMs
Traditionally, computer vision models process images in a sequential manner, which can be computationally expensive and time-consuming. ViTTMs offer a new perspective by allowing for non-sequential visual understanding. By creating two sets of tokens – process tokens and memory tokens – ViTTMs enable information storage and retrieval from memory as the process tokens pass through individual encoder blocks.
By having fewer process tokens than memory tokens, ViTTMs significantly reduce inference time without compromising accuracy. This ensures that the model can make efficient use of memory while maintaining top-notch performance.
Impressive Results
Initial evaluations of ViTTMs on benchmark datasets have yielded impressive results. For instance, on the widely-used ImageNet-1K dataset, the state-of-the-art ViT-B achieves a median latency of 529.5ms and an accuracy of 81.0%. However, the ViTTM-B model outperforms it, boasting a faster inference time of 234.1ms (a 56% improvement) and 82.9% accuracy. Moreover, the ViTTM-B model achieves these results using only 2.4 times fewer Floating-Point Operations (FLOPs).
In the context of ADE20K semantic segmentation, the ViT-B model achieves 45.65 mIoU (mean Intersection over Union) at a frame rate of 13.8 FPS. On the other hand, the ViTTM-B model achieves a slightly lower mIoU of 45.17 but at a significantly improved frame rate of 26.8 FPS (+94%). This demonstrates the potential of ViTTMs in boosting the efficiency of complex computer vision tasks.
Innovation for the Future
ViTTMs open up exciting possibilities in the field of computer vision, paving the way for more efficient and accurate models in various real-world applications. Although ViTTMs are currently mainly applied to image classification and semantic segmentation, their potential for other non-sequential computer vision tasks is vast.
Further research could explore the use of ViTTMs in areas such as object detection, video understanding, and visual reasoning. By harnessing the power of memory-augmented models like ViTTMs, researchers have the opportunity to push the boundaries of computer vision and create groundbreaking solutions.
ViTTMs represent an innovative approach to non-sequential computer vision tasks, offering a balance between efficiency and accuracy. With their ability to store and retrieve information from memory, these models hold tremendous potential for revolutionizing various computer vision applications. As researchers continue to explore and refine the ViTTM framework, we can look forward to seeing even more impressive results in the future.
The proposed Vision Token Turing Machines (ViTTM) presented in the arXiv paper aim to enhance the efficiency and performance of Vision Transformers (ViT) for non-sequential computer vision tasks like image classification and segmentation. Building upon the concepts of Neural Turing Machines and Token Turing Machines, which were previously applied to natural language processing and sequential visual understanding tasks, the authors introduce a novel approach that leverages process tokens and memory tokens.
In the ViTTM architecture, process tokens and memory tokens play distinct roles. Process tokens are passed through encoder blocks and interact with memory tokens, allowing for information storage and retrieval. The key insight here is that by having fewer process tokens than memory tokens, the inference time of the network can be reduced while maintaining accuracy.
The experimental results presented in the paper demonstrate the effectiveness of ViTTM compared to the state-of-the-art ViT-B model. On the ImageNet-1K dataset, ViTTM-B achieves a median latency of 234.1ms, which is 56% faster than ViT-B with a latency of 529.5ms. Additionally, ViTTM-B achieves an accuracy of 82.9%, slightly surpassing ViT-B’s accuracy of 81.0%. This improvement in both speed and accuracy is significant, especially considering that ViTTM-B requires 2.4 times fewer FLOPs (floating-point operations) than ViT-B.
Furthermore, the authors evaluate the performance of ViTTM on the ADE20K semantic segmentation task. ViT-B achieves a mean intersection over union (mIoU) of 45.65 at 13.8 frames per second (FPS). In contrast, the proposed ViTTM-B model achieves a slightly lower mIoU of 45.17 but significantly boosts the FPS to 26.8 (+94%). This trade-off between accuracy and speed is a common consideration in computer vision applications, and ViTTM-B provides a compelling option for real-time semantic segmentation tasks.
Overall, the introduction of Vision Token Turing Machines (ViTTM) offers a promising approach to improve the efficiency and performance of Vision Transformers for non-sequential computer vision tasks. The experimental results demonstrate the effectiveness of ViTTM in reducing inference time while maintaining competitive accuracy levels. This work opens up new possibilities for applying memory-augmented models to various computer vision applications and may inspire further research in this direction.
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