Neural Information Processing Systems http://nips.cc/

Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images.

In this supplementary material, we provide additional implementation details (Appendix A) of our model and visualization of ablation studies (Appendix B) which are not included in our main paper. BARF-W, and BARF-WD are based on [2] because there is no official NeRF-W code available. The detailed architecture of UP-NeRF is shown in the Figure 1. First two authors have an equal contribution. As we mentioned in the main paper, the evaluation process entails two stages, which are test-time pose optimization and appearance optimization.

Recently, researchers in Vision-Language (VL) domains also develop their few-shot learners, while they only use the simplest way, i .

During the CL of VLMs, we need not only to prevent the catastrophic forgetting on incrementally learned knowledge but also to preserve the zero-shot ability of VLMs.