Efficient Vision Transformer Architecture for High-Throughput Computer Vision Applications

Основні поняття

FasterViT is a novel hybrid CNN-ViT architecture that achieves state-of-the-art performance in terms of accuracy and image throughput for computer vision applications. It combines the benefits of fast local representation learning in CNNs and global modeling properties in ViTs, using a newly introduced Hierarchical Attention (HAT) approach to efficiently capture both short and long-range spatial dependencies.

Анотація

The paper introduces FasterViT, a novel hybrid CNN-ViT neural network architecture designed for high image throughput in computer vision applications. The key contributions are:

FasterViT combines the strengths of CNNs and ViTs, using CNNs in the early stages for fast local representation learning and ViT-based blocks in later stages for global modeling.

The proposed Hierarchical Attention (HAT) mechanism decomposes the global self-attention in ViTs into a multi-level attention with reduced computational costs. HAT uses carrier tokens to summarize each local window and efficiently model the cross-window interactions.

Experiments show that FasterViT achieves a new state-of-the-art Pareto front in terms of ImageNet-1K top-1 accuracy and image throughput, outperforming recent models like ConvNeXt and Swin Transformer. It also demonstrates competitive performance on downstream tasks like object detection, instance segmentation, and semantic segmentation.

The authors validate the scalability of FasterViT by pre-training on the larger ImageNet-21K dataset and fine-tuning on various high-resolution inputs, showing significant improvements in accuracy-throughput trade-off compared to other counterparts.

Ablation studies confirm the effectiveness of the proposed HAT module, which can also be used as a plug-and-play component to enhance existing architectures like Swin Transformer.

Статистика

FasterViT-4 achieves 86.6% top-1 accuracy on ImageNet-1K with a throughput of 849 images/sec on A100 GPU.
FasterViT-4 pre-trained on ImageNet-21K and fine-tuned on ImageNet-1K achieves 87.5% top-1 accuracy at 281 images/sec.
FasterViT-2 achieves 52.1 box AP and 45.2 mask AP on MS COCO object detection and instance segmentation, with a throughput of 287 images/sec.
FasterViT-3 achieves 48.7 mIoU on ADE20K semantic segmentation at 254 images/sec.

Цитати

"FasterViT achieves a new SOTA Pareto front in terms of image throughput and accuracy trade-off and is significantly faster than comparable ViT-based architectures yielding significant speed-up compared to recent SOTA models."
"We propose the Hierarchical Attention module which efficiently captures the cross-window interactions of local regions and models the long-range spatial dependencies."

Ключові висновки, отримані з

FasterViT

by Ali Hatamiza... о arxiv.org 04-03-2024

https://arxiv.org/pdf/2306.06189.pdf

Глибші Запити

How can the proposed Hierarchical Attention mechanism be further extended to capture higher-order interactions between local and global features?

The proposed Hierarchical Attention mechanism can be extended to capture higher-order interactions by incorporating multiple levels of hierarchy in the attention process. Currently, the mechanism involves summarizing local windows with carrier tokens and then performing attention between these carrier tokens to capture global information exchange. To enhance this further, additional layers of hierarchical attention can be introduced, where the carrier tokens from one level can serve as inputs to another level of hierarchical attention. This way, the model can capture interactions not only between local and global features but also across multiple levels of abstraction. By introducing more levels of hierarchy, the model can effectively capture complex relationships and dependencies between features at different scales.

What are the potential limitations of the FasterViT architecture, and how could it be adapted to handle extremely high-resolution inputs or specialized computer vision tasks?

One potential limitation of the FasterViT architecture could be its scalability to handle extremely high-resolution inputs. As the input resolution increases, the computational complexity of the model also grows, which can impact the efficiency and throughput of the model. To adapt the FasterViT architecture for extremely high-resolution inputs, strategies such as efficient downsampling techniques, sparse attention mechanisms, or hierarchical processing can be employed. By incorporating these techniques, the model can effectively handle larger input sizes without compromising performance.
For specialized computer vision tasks, the FasterViT architecture may need modifications to address specific requirements. For instance, for tasks requiring fine-grained details or precise localization, the model may benefit from incorporating attention mechanisms that focus on capturing intricate spatial relationships. Additionally, for tasks with limited training data, techniques like transfer learning or domain adaptation can be applied to fine-tune the FasterViT model for improved performance on specialized tasks.

Given the impressive performance of FasterViT on ImageNet-21K, how could the pre-training and fine-tuning strategies be leveraged to improve transfer learning capabilities for a broader range of computer vision applications?

The pre-training and fine-tuning strategies used for FasterViT on ImageNet-21K can be leveraged to enhance transfer learning capabilities for a broader range of computer vision applications. One approach is to transfer the knowledge learned from ImageNet-21K pre-training to downstream tasks by fine-tuning the model on specific datasets related to the target application. By fine-tuning the pre-trained FasterViT model on task-specific data, the model can adapt its learned representations to the nuances of the new dataset, leading to improved performance.
Furthermore, the pre-trained FasterViT model can serve as a strong feature extractor for various computer vision tasks. By leveraging the representations learned during pre-training, the model can be used as a feature encoder in transfer learning pipelines, where the extracted features can be fed into task-specific classifiers or regression models. This approach enables the model to generalize well to diverse tasks and datasets, showcasing its versatility and transfer learning capabilities across a wide range of computer vision applications.