During the inference, we set the background score as 0.95.

Then, we present the error bar plots with mean D-ECE and std deviation (Sec. The error in particular detection is computed as it satisfies the false positive criteria. We report D-ECE on these challenging out-domain scenarios. (Figure 1). We show the bar plots depicting mean D-ECE with respective standard deviations.

All RSTB blocks consist of 6 STB blocks. Each sequence contains 33 frames. Blurry images with different motion magnitudes are generated by averaging the surrounding 33 or 65 images. S1, we observe that the introduction of CAMMA also improves the performance of de-blurring across all settings. We have added comparisons regarding computational complexity and inference time in Tab.

The number of part queries is set to 50. SGD optimizer with the initial learning rate of 2e-2 and weight decay of 5e-4 is used. We sample 128 training samples for each object part class. The initial value of the learnable fusion weight is 0.5 . The total batch size is 8, and the training iterations amount to 40k.

These metals are supposed as Titanium. Detailed parameters of the acquisition geometry can be found in Table 1. This sample is 3D cone-beam data. The estimated spectrum is illustrated in Figure 1 ( Right). 2 2 Additional Details of Baselines In our experiments, we compare our proposed method against eight baseline MAR approaches. Specifically, it learns the prior distribution of metal-free CT images with a generative model in order to infer the lost sinogram in the metal-affected regions.

DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs.

It represents a radiance field using tri-planes with three multi-resolutions for each plane: 128, 256, and 512 in both height and width, and 32 in feature depth. However, any MDE model can be utilized within our framework [19, 13, 12]. The training process takes approximately 3 hours. In other words, we can rewrite the above scheme as a closed problem. The results of DDP-NeRF with in-domain priors are 20.96,