The Latest CNN Kernels for Large Image Models | What’s New in CNN Kernels? | By Wanming Huang | August 2023

The Latest CNN Kernels for Large Image Models | What’s New in CNN Kernels? | By Wanming Huang | August 2023

Introduction:

The field of large image models is experiencing a surge in advancements, much like the success seen with OpenAI’s ChatGPT. Vision models are now capable of analyzing and generating images and videos in a manner similar to prompt-based language models. This article focuses on the convolutional neural network (CNN) side of large image models, specifically exploring the latest improvements in convolutional kernel structures. Traditional CNN kernels had fixed locations in each layer, limiting their ability to handle objects with varying scales. However, deformable convolutional networks (DCN), including DCNv2 and DCNv3, offer more flexibility in modeling geometric structures by allowing for adjustments to the receptive field. DCNv2 introduces modulated deformable modules, while DCNv3 incorporates depthwise separable convolution and group convolution techniques. These advancements have demonstrated superior performance in object detection and instance segmentation tasks. For a more comprehensive understanding, refer to the papers listed in the References section.

Full Article: The Latest CNN Kernels for Large Image Models | What’s New in CNN Kernels? | By Wanming Huang | August 2023

A High-Level Overview of the Latest Convolutional Kernel Structures in Deformable Convolutional Networks

As large language models like OpenAI’s ChatGPT continue to gain popularity, there has been increasing interest in developing large image models that can analyze and generate images and videos. In this domain, vision models can be prompted in a similar manner to how ChatGPT is prompted. One of the key areas of focus in large image models is the improvement of convolutional neural networks (CNNs). This article will provide a high-level overview of the latest convolutional kernel structures in Deformable Convolutional Networks (DCN).

Traditional CNN Kernels and their Limitations

In traditional CNNs, kernels are applied to fixed locations in each layer, resulting in all activation units having the same receptive field. This means that the size and shape of the receptive field remains the same across all locations within the same layer. While this approach works well for lower-level layers, it becomes problematic for higher-level layers that encode semantics and objects with varying scales. Regular convolution and RoI pooling operations with fixed size bins are not flexible enough to model the geometric structures of objects with varying scales.

Deformable Convolution and Deformable RoI Pooling

To address the limitations of traditional CNN kernels, Deformable Convolution (DCN) introduces deformable convolution and deformable RoI pooling. These operations operate on the 2D spatial domain and provide more flexibility in modeling geometric structures. In deformable convolution, 2D offsets are added to each location in the output feature map, allowing for more flexible modeling of the receptive field. These offsets are learned from preceding feature maps using an additional convolutional layer. Deformable RoI pooling applies similar offsets to the original binning positions in RoI pooling, allowing for the modeling of objects with varying scales.

Deformable Position-Sensitive RoI Pooling

For position-sensitive RoI pooling, deformable operations are applied to each score map instead of the input feature map. These operations involve applying offsets to each score map to retain information about which object part each region represents. The offsets are learned through a convolutional layer instead of a fully connected layer.

DCNv2 and DCNv3: Improving Deformable Convolution

DCNv2 introduces modulated deformable modules, where learnable feature amplitudes are assigned to locations within the receptive field. This allows for better understanding of the contribution behavior of each location. DCNv2 also expands the use of deformable convolution layers in the ResNet-50 architecture.

DCNv3 builds upon DCNv2 and makes adjustments to the kernel structure. It introduces depthwise separable convolution, which decouples traditional convolution into depth-wise convolution and point-wise convolution. In DCNv3, the feature amplitude and projection weight are shared among locations in the grid. It also incorporates group convolution, where the convolution is split into groups, each having separate offsets and feature amplitudes.

Superior Performance and Future Directions

DCNv3, specifically based InternImage, has demonstrated superior performance in various downstream tasks such as object detection and instance segmentation. It currently holds a top position on the leaderboard for object detection on paperswithcode.com.

In Conclusion

In this article, we have provided a high-level overview of the latest convolutional kernel structures in Deformable Convolutional Networks (DCN). We discussed the limitations of traditional CNN kernels and highlighted the advancements made in DCN, DCNv2, and DCNv3. These improvements have led to better modeling of geometric structures, improved performance in various tasks, and recognition on the leaderboard for object detection. For more detailed information, we encourage you to refer to the original papers in the References section.

References:
– Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H. and Wei, Y. (n.d.). Deformable Convolutional Networks.
– Zhu, X., Hu, H., Lin, S. and Dai, J. (n.d.). Deformable ConvNets v2: More Deformable, Better Results.
– Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., Wang, X. and Qiao, Y. (n.d.). InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions.
– Chollet, F. (n.d.). Xception: Deep Learning with Depthwise Separable Convolutions.
– Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks.
– Dai, J., Li, Y., He, K. and Sun, J. (n.d.). R-FCN: Object Detection via Region-based Fully Convolutional Networks.

Summary: The Latest CNN Kernels for Large Image Models | What’s New in CNN Kernels? | By Wanming Huang | August 2023

The latest advancements in convolutional kernel structures, specifically in Deformable Convolutional Networks (DCN), have revolutionized large image models. Traditionally, convolutional neural networks (CNNs) applied fixed kernels at fixed locations, limiting their flexibility in modeling geometric structures. DCN introduces deformable convolution and deformable pooling, allowing for more adaptable modeling of varying scales and receptive fields. DCNv2 builds upon this by introducing modulated deformable modules that assign learnable feature amplitude to locations within the receptive field. DCNv3 further enhances the kernel structure by leveraging depthwise separable convolution and group convolution techniques. These advancements have led to superior performance in object detection and segmentation tasks.

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