K-Origins提升颜色感知能力，增强图像分割精度。
K-Origins: Better Colour Quantification for Neural Networks
- 通过可训练参数调整输入特征，实现更优的颜色量化
- 在低信噪比和同形异色物体分割中准确率显著提升
- 适合需要精细颜色区分的视觉任务研究者
K-Origins 是一种神经网络层，旨在提升图像相关网络在学习颜色或强度时的表现。在16位合成数据上，对超过250个编码器-解码器卷积网络进行训练与测试，结果表明，在低信噪比下的目标检测以及形状相同但颜色不同的多对象分割场景中，K-Origins均能提升语义分割准确率。该方法通过公式 $\textbf{Y}_k = \textbf{X}-\textbf{J}\cdot w_k$ 从输入特征 $\textbf{X}$ 生成输出特征，其中 $\textbf{J}$ 为全1矩阵。此外，通过训练不同感受野的网络，发现最优网络深度应使感受野长度超过目标对象尺寸。结合充分的感受野与K-Origins，可进一步提升语义网络性能。
原文摘要 · Abstract (English)
K-Origins is a neural network layer designed to improve image-based network performances when learning colour, or intensities, is beneficial. Over 250 encoder-decoder convolutional networks are trained and tested on 16-bit synthetic data, demonstrating that K-Origins improves semantic segmentation accuracy in two scenarios: object detection with low signal-to-noise ratios, and segmenting multiple objects that are identical in shape but vary in colour. K-Origins generates output features from the input features, $\textbf{X}$, by the equation $\textbf{Y}_k = \textbf{X}-\textbf{J}\cdot w_k$ for each trainable parameter $w_k$, where $\textbf{J}$ is a matrix of ones. Additionally, networks with varying receptive fields were trained to determine optimal network depths based on the dimensions of target classes, suggesting that receptive field lengths should exceed object sizes. By ensuring a sufficient receptive field length and incorporating K-Origins, we can achieve better semantic network performance.
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