In the supplementary material, we first introduce the pseudo algorithm of DA W . Then we clarify the Then, we provide a more detailed explanation of Figures 1, 2, 4, and 5, which are slightly abbreviated due to the limited space of the main paper. In the naive pseudo-labeling method, all pseudo labels are enrolled into training, i.e., E 1 + E 2, which is guaranteed by theoretical functional analysis in the next section. Inequality 45 holds true at all times. In this section, we provide more qualitative results between ours and other competitors.

In Algorithm 1 we give the full clustering algorithm used for each of the T fixing iterations. In Figure 1 we show how the layers' In Figure 2 we show the impact of increasing the regularisation strength.

Our approach can be naturally extended to include multiple examples. Below we discuss the impact of these examples on our model's final performance by varying their Similarly, in the third row, the wormhole becomes complete. In our work, we have developed a human interface to further enhance our model's ability to understand Additionally, the dress before the chest area is better preserved. Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

The computational overhead of CluB is 1.2 / 1.3 times that of the BEV -only A detailed comparison is shown in the following table. GPUs and the batch size per GPU is set as 2. Table 2: Ablation study on the effect of the two kinds of object queries for the transformer decoder. Red boxes and green boxes are the predictions and ground-truth, respectively. Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. Fully sparse 3d object detection.

We thank all reviewers for their constructive comments and address the raised issues below. As described in Secion 3.2 of the manuscript, we introduce the The source code, as mentioned on L141, will be made available to the public. R1: Why the adaptive flow filtering is a better way of reducing artifacts? Our method could be seen as a learnable median filter in spirit. Although the quantitative improvement from the adaptive flow filtering (ada.) is small, this component is important in generating results with higher visual quality SepConv has originally been trained on high-quality videos with large motion.

We thank the reviewers for their comments. We then address reviewer's comments individually (due to space limits please zoom in the tiny figures). For [18] we used Alg. 2 We thank the reviewer for the additional reference, which we will add to the paper. Gradient Descent) applied in parallel to multiple starting points. We thank R2 for the reference "Entropic regularization of continuous optimal transport problems".