Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks.

Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality.

Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods.

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.

Source code is made available at https://github.com/ymLeiFDU/LICO.

Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes.

Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.