Transformers have achieved tremendous success in various computer vision tasks. By borrowing design concepts from transformers, many studies revolutionized CNNs and showed remarkable results. This paper falls in this line of studies. More specifically, we introduce a convolutional neural network architecture named ParCNetV2, which extends position-aware circular convolution (ParCNet) with oversized convolutions and strengthens attention through bifurcate gate units. The oversized convolution utilizes a kernel with $2\times$ the input size to model long-range dependencies through a global receptive field. Simultaneously, it achieves implicit positional encoding by removing the shift-invariant property from convolutional kernels, i.e., the effective kernels at different spatial locations are different when the kernel size is twice as large as the input size. The bifurcate gate unit implements an attention mechanism similar to self-attention in transformers. It splits the input into two branches, one serves as feature transformation while the other serves as attention weights. The attention is applied through element-wise multiplication of the two branches. Besides, we introduce a unified local-global convolution block to unify the design of the early and late stage convolutional blocks. Extensive experiments demonstrate that our method outperforms other pure convolutional neural networks as well as neural networks hybridizing CNNs and transformers.

翻译：变异器在各种计算机愿景任务中取得了巨大成功。 通过从变压器中借用设计概念, 许多研究使CNN革命, 并展示了显著的成果。 本文属于这一系列的研究。 更具体地说, 我们引入了名为 ParCNetV2 的进动神经网络结构结构, 扩展了位置意识循环变异( ParCNet), 其规模过大, 并通过双向门装置加强了注意力。 过度变异利用了一个内核, 其投入规模为2美元, 通过一个全球可接受域, 模拟远程依赖性。 同时, 它通过从共进核内核中去除变异特性, 即, 当不同空间位置的有效内核网络的大小比投入规模大一倍时, 则不同。 双向门单元采用了类似于变异器自我保护的注意机制。 它将投入分为两个分支, 一个作为特征变异功能, 而另一个分支作为关注的分量。 它通过两部变异的元素和多变异特性来实现定位 。 此外, 我们引入了一个内部变异系统, 的系统, 也引入了一个系统, 革命系统, 向了一个新的系统,, 向 革命系统,, 新的系统, 新的系统 的系统 的系统将一个系统,,, 新的系统, 向后进变异变异变的系统将一个系统,, 向 向 向 的系统 的系统 向 向 向 向 向 向 向 。