Implicit Neural Representations (INRs) leverage neural networks to map coordinates to corresponding signals, enabling continuous and compact representations. This paradigm has driven significant advances in various vision tasks. However, existing INRs lack frequency selectivity and spatial localization, leading to an over-reliance on redundant signal components. Consequently, they exhibit spectral bias, tending to learn low-frequency components early while struggling to capture fine high-frequency details. To address these issues, we propose FLAIR (Frequency- and Locality-Aware Implicit Neural Representations), which incorporates two key innovations. The first is Band-Localized Activation (BLA), a novel activation designed for joint frequency selection and spatial localization under the constraints of the time-frequency uncertainty principle (TFUP). Through structured frequency control and spatially localized responses, BLA effectively mitigates spectral bias and enhances training stability. The second is Wavelet-Energy-Guided Encoding (WEGE), which leverages the discrete wavelet transform to compute energy scores and explicitly guide frequency information to the network, enabling precise frequency selection and adaptive band control. Our method consistently outperforms existing INRs in 2D image representation, as well as 3D shape reconstruction and novel view synthesis.

翻译：隐式神经表示（INR）利用神经网络将坐标映射至对应信号，实现了连续且紧凑的表示。该范式推动了多种视觉任务的重大进展。然而，现有INR缺乏频率选择性与空间定位能力，导致对冗余信号成分的过度依赖。因此，它们表现出频谱偏差，倾向于早期学习低频分量，而难以捕捉精细的高频细节。为解决这些问题，我们提出FLAIR（频率与位置感知隐式神经表示），包含两项核心创新。其一是频带定位激活函数（BLA），一种在时间-频率不确定原理（TFUP）约束下专为联合频率选择与空间定位设计的新型激活函数。通过结构化的频率控制与空间定位响应，BLA有效缓解频谱偏差并增强训练稳定性。其二是小波能量引导编码（WEGE），该方法利用离散小波变换计算能量分数，显式引导频率信息输入网络，实现精确的频率选择与自适应频带控制。在二维图像表示、三维形状重建及新视角合成任务中，我们的方法始终优于现有INR方法。