  Implicit Neural Representation (INR) has gained increasing popularity as a
data representation method, serving as a prerequisite for innovative generation
models. Unlike gradient-based methods, which exhibit lower efficiency in
inference, the adoption of hyper-network for generating parameters in
Multi-Layer Perceptrons (MLP), responsible for executing INR functions, has
surfaced as a promising and efficient alternative. However, as a global
continuous function, MLP is challenging in modeling highly discontinuous
signals, resulting in slow convergence during the training phase and inaccurate
reconstruction performance. Moreover, MLP requires massive representation
parameters, which implies inefficiencies in data representation. In this paper,
we propose a novel Attention-based Localized INR (ANR) composed of a localized
attention layer (LAL) and a global MLP that integrates coordinate features with
data features and converts them to meaningful outputs. Subsequently, we design
an instance representation framework that delivers a transformer-like
hyper-network to represent data instances as a compact representation vector.
With instance-specific representation vector and instance-agnostic ANR
parameters, the target signals are well reconstructed as a continuous function.
We further address aliasing artifacts with variational coordinates when
obtaining the super-resolution inference results. Extensive experimentation
across four datasets showcases the notable efficacy of our ANR method, e.g.
enhancing the PSNR value from 37.95dB to 47.25dB on the CelebA dataset. Code is
released at https://github.com/Roninton/ANR.
