VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis (2412.18178v2)

Abstract: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are two dominant models for image analysis. While CNNs excel at extracting multi-scale features and ViTs effectively capture global dependencies, both suffer from high computational costs, particularly when processing high-resolution images. Recently, state-space models (SSMs) and recurrent neural networks (RNNs) have attracted attention due to their efficiency. However, their performance in image classification tasks remains limited. To address these challenges, this paper introduces VisionGRU, a novel RNN-based architecture designed for efficient image classification. VisionGRU leverages a simplified Gated Recurrent Unit (minGRU) to process large-scale image features with linear complexity. It divides images into smaller patches and progressively reduces the sequence length while increasing the channel depth, thus facilitating multi-scale feature extraction. A hierarchical 2DGRU module with bidirectional scanning captures both local and global contexts, improving long-range dependency modeling, particularly for tasks like semantic segmentation. Experimental results on the ImageNet and ADE20K datasets demonstrate that VisionGRU outperforms ViTs, significantly reducing memory usage and computational costs, especially for high-resolution images. These findings underscore the potential of RNN-based approaches for developing efficient and scalable computer vision solutions. Codes will be available at https://github.com/YangLiu9208/VisionGRU.

• The paper presents VisionGRU, a novel RNN-based architecture using a simplified minGRU that achieves linear computational complexity versus quadratic transformer models.
• It employs a bidirectional 2DGRU module to enhance long-range dependency modeling, enabling efficient handling of high-resolution images for tasks like semantic segmentation.
• Experimental results show that VisionGRU-Ti attains 82% accuracy on ImageNet with significantly lower memory usage and GFLOPs compared to contemporary models.

An Analysis of VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis

The presented paper introduces VisionGRU, a novel recurrent neural network (RNN)-based architecture designed to address computational efficiency challenges in image analysis. The motivation behind this paper stems from the limitations of traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in handling the high computational costs associated with processing high-resolution images. VisionGRU leverages a simplified version of the Gated Recurrent Unit, termed minGRU, to offer a linear complexity solution, marking a departure from the typical quadratic complexity found in transformer models.

Methodological Insights

VisionGRU capitalizes on the inherent efficiency of RNNs, integrating the minGRU structure to eliminate the need for backpropagation through time (BPTT) and foster parallel acceleration. This design simplifies the computational flow by reducing the parameter count and facilitating the handling of large-scale image features, making it a suitable candidate for high-resolution image processing tasks like semantic segmentation.

The architecture is further refined by incorporating a bidirectional scanning mechanism through a novel module, 2DGRU, which enhances the RNN's capacity to model long-range dependencies. This is achieved by a hierarchical approach to feature extraction, where input images are divided into smaller patches, progressively reducing the sequence length while increasing the channel depth.

Experimental Findings

Performance evaluation of VisionGRU was conducted on benchmark datasets, notably ImageNet and ADE20K, demonstrating its superior efficacy over contemporary ViT models. VisionGRU-Ti, a compact version of the model, achieved a classification accuracy of 82%, surpassing the performance of the DeiT-S model while exhibiting a significant reduction in memory usage and computational costs—requiring 151.9 GFLOPs compared to DeiT-S's 432.3 GFLOPs.

In the context of semantic segmentation, VisionGRU's architecture afforded it a balanced ability to capture both local details and global context, reflecting in a higher mIoU score. Such numerical results affirm the architecture's scalability and its potential in efficiently tackling tasks necessitating multi-scale feature extraction and context aggregation.

Concluding Remarks and Future Perspectives

The introduction of VisionGRU highlights the viability of RNN-based architectures in the computer vision domain, providing a compelling alternative to prevalent deep learning models for image classification and segmentation tasks. The model’s linear complexity and reduced memory footprint suggest its applicability in scenarios where computational resources are constrained.

In consideration of future developments, VisionGRU may pave the way for RNN variants optimized for other complex visual tasks beyond classification and segmentation, such as video analysis and real-time image processing applications. Its design principles could influence ongoing research efforts to integrate computational efficiency with high accuracy in AI models, possibly inspiring further exploration into efficient model structures that transcend traditional architectural choices. This research expands the toolkit available to AI practitioners, reinforcing the role of RNNs in pioneering scalable, efficient solutions in computer vision.