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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