```html

Efficient Image Processing on Mobile Devices: iFormer Combines Convolutional Networks and Transformers

The world of mobile applications places high demands on the efficiency of AI models. Fast response times and low resource consumption are crucial, especially for computationally intensive tasks like image processing. A promising approach to addressing this challenge is the combination of different network architectures. iFormer, a new family of hybrid vision networks, pursues precisely this approach and integrates the strengths of Convolutional Neural Networks (CNNs) and Transformers.

CNNs have proven themselves in image processing because they can efficiently detect local patterns. Transformers, on the other hand, are characterized by their ability to model global relationships in data. iFormer combines these two approaches to utilize both the fast local processing of CNNs and the global contextualization of Transformers. The result is a network that achieves high accuracy with low latency.

The foundation of iFormer is a further development of the established CNN model ConvNeXt. This modification aims to optimize the architecture for mobile applications and reduce computational costs. However, the innovative aspect of iFormer lies in the integration of a so-called "Mobile Modulation Attention." This mechanism replaces the memory-intensive operations of the classic Multi-Head Attention (MHA) in Transformers with an efficient modulation mechanism. This dynamically increases the network's global representation capability without impacting performance on mobile devices.

Extensive tests demonstrate the performance of iFormer. Compared to existing lightweight networks, iFormer achieves compelling results in various image processing tasks. For example, iFormer achieves a Top-1 accuracy of 80.4% on the ImageNet-1k dataset with a latency of only 1.10 ms on an iPhone 13. This surpasses the recently introduced MobileNetV4 with comparable latency constraints.

The advantages of iFormer are also evident in more demanding tasks such as object detection, instance segmentation, and semantic segmentation. Here too, the model achieves significant improvements while maintaining low latency on mobile devices, even with high-resolution images.

Applications and Future Perspectives

The efficient architecture of iFormer opens up a wide range of application possibilities in the field of mobile AI applications. From image search and classification to augmented reality and robotics – wherever fast and resource-saving image processing is required, iFormer can make a decisive contribution. The combination of local and global information processing makes it possible to precisely analyze and interpret complex image content without compromising the performance of mobile devices.

The development of iFormer is an important step towards more powerful and efficient AI models for mobile applications. Future research could focus on further optimizing the architecture and adapting it to specific use cases. The integration of iFormer into platforms like Mindverse enables developers to leverage the benefits of this technology for their own projects and develop innovative mobile applications.

Bibliographie: Chen, C.-F. et al. (2022). Mobile-Former: Bridging MobileNet and Transformer. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*. Zheng, C. (2025). iFormer: Integrating ConvNet and Transformer for Mobile Application. *arXiv preprint arXiv:2501.15369*. Dwivedi, V. P. & Srivastava, G. (2024). A Deep Learning-Based Intrusion Detection Model Integrating Convolutional Neural Network and Vision Transformer for Network Traffic Attack in the Internet of Things. *arXiv preprint arXiv:2411.07118*. Vaswani, A. et al. (2017). Attention is All you Need. *Advances in Neural Information Processing Systems*, *30*. ```