Hi, thanks for the great work! I have a small question about the experimental claims in Table 2.
There is a performance decline (from 9.5 to 6.3) after introducing novel-class object proposals (# 2 Table 2 ) for self-training. I am confused about the explanation given in the paper: "Because we do not know the category id of these object proposals, we observe that the label assignment of these object proposals is inaccurate". However, I noticed that the specific pseudo-label of novel classes has been given in "coco_train2017_seen_2_proposal.json", which is a little bit noisy.
Moreover, could you please provide the generated proposals for the LVIS dataset, i.e., lvis_train2017_seen_2_proposal.json and configs? I am following your great work and hope to keep a fair comparison with the same data usage.
Hi, thanks for the great work! I have a small question about the experimental claims in Table 2.
There is a performance decline (from 9.5 to 6.3) after introducing novel-class object proposals (# 2 Table 2 ) for self-training. I am confused about the explanation given in the paper: "Because we do not know the category id of these object proposals, we observe that the label assignment of these object proposals is inaccurate". However, I noticed that the specific pseudo-label of novel classes has been given in "coco_train2017_seen_2_proposal.json", which is a little bit noisy.
Moreover, could you please provide the generated proposals for the LVIS dataset, i.e., lvis_train2017_seen_2_proposal.json and configs? I am following your great work and hope to keep a fair comparison with the same data usage.
Thank you so much!