论文标题
重新思考视觉学习者的隐性神经表示
Rethinking Implicit Neural Representations for Vision Learners
论文作者
论文摘要
隐式神经表示（INRS）具有强大的功能，可以参数化计算机视觉中的连续信号。但是，几乎所有INRS方法都限于低级任务，例如图像/视频压缩，超分辨率和图像生成。关于如何探索INR到高级任务和深层网络的问题仍然不足。现有的INR方法遇到了两个问题：1）INR的狭窄理论定义不适合高级任务； 2）缺乏深层网络的代表性功能。在上述事实的激励下，我们从新颖的角度重新重新制定了INR的定义，并提出了一个创新的隐式神经代表网络（INRN），这是INR的首次研究，以解决低级和高级任务。具体而言，我们为INRN中的基本块提供了三个关键设计，以及两种不同的堆叠方式和相应的损失功能。对低级任务（图像拟合）和高级视觉任务（图像分类，对象检测，实例分割）进行分析的广泛实验证明了所提出的方法的有效性。
Implicit Neural Representations (INRs) are powerful to parameterize continuous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. The questions on how to explore INRs to high-level tasks and deep networks are still under-explored. Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks; 2) lack of representation capabilities to deep networks. Motivated by the above facts, we reformulate the definitions of INRs from a novel perspective and propose an innovative Implicit Neural Representation Network (INRN), which is the first study of INRs to tackle both low-level and high-level tasks. Specifically, we present three key designs for basic blocks in INRN along with two different stacking ways and corresponding loss functions. Extensive experiments with analysis on both low-level tasks (image fitting) and high-level vision tasks (image classification, object detection, instance segmentation) demonstrate the effectiveness of the proposed method.
