Hi @jbeomlee93 I have some questions for the equations of you paper. That is: 1. For equation(1), the paper[16] uses x' = x + gradient to adversarial attack the input image (i.e., X) and finally generate the image Xadv, which is opposited to your equation (1) (i.e., x' = x - gradient ). 2. For equation(3), the paper[28] uses iter(x' = x + gradient) to represents the adversarial attack process, which is same to equation (3)(i.e., x' = x + gradient ). I think adversarial climbing should be opposited to the adversarial attack. Sincerely waiting for your reply !

in the paper you mentioned and showed an example of visualizing the loss landscape. Can you please explain how to do it ? I would really appreciate it if you can share the code for that since I am new to this topic and your implementation will be super useful!

Hi, I ran your codes and apply all the hyper-parameters by default. But the init seed mIoU is only 48.75, which is reported as 55.6 in your paper. I wonder if i miss something? Here is my train command line: CUDA_VISIBLE_DEVICES=4,5,6,7 python run_sample.py --voc12_root Dataset/VOC2012_SEG_AUG/ --cam_out_dir result/cam_res50 --sem_seg_out_dir result/seg_res50 --ir_label_out_dir result/ir_label_res50 --irn_weights_name sess/res50_irn.pth --make_ins_seg_pass False --eval_ins_seg_pass False

And here is my init seed result:

Thanks for the great work. I have a question about IRN. Have you reproduced the performance of IRN? I run the code of IRN, but the results are not as good as the paper of IRN. In your implementation, will it affect the performance of AdvCAM?

Thank you in advance for your help.

Thanks for sharing your code. after downloading all the materials now I am running the bash get_mask_quality.sh but it is super slow and I am wondering how long that process should take? and what does that _work function in obtain_CAM_masking.py really do? do I need to do it?

Thanks for sharing this great work!

Just wondering if you could also share the code that reproduces table 4, especially AdvCAM with SEAM.

I found the SEAM GitHub and I assume that your AdvCAM should be replaced with cam in SEAM code as shown in the below link [https://github.com/YudeWang/SEAM/blob/c55601649c5fa676836d3ec70ec044541b7d1d83/network/resnet38_SEAM.py#L32]

Could you please guide me on how to merge your AdvCAM into the SEAM module?

Thanks

Hi, Thanks for your code and paper! Congratulations on your paper being accepted by TPAMI! I notice that in the version of TPAMI, additional experimental results on MS COCO dataset and ImageNet1k are presented. May I trouble you to release these code and fine-turned models? Thanks!

@jbeomlee93 ! Sorry for disturbing you again. I still have two questions about the released code. (1) In obtain_CAM_masking_super_pixel.py. Since you have used grad-cam to generate the class activation map(i.e., CAM), why don't you use resnet50.py with grad-cam to generate outputs. Actually, you used resnet50_cam.py with grad-cam to generate the outputs.

(2) Can you share the code of "SEAM+AdvCAM" with me. I try to reproduce it by myself but the performance is not good as yours. My email address is [email protected]

Hi，thanks for your sharing!But i still have some questions look forward to your answer! is the result of the obtian_cam_masking.py a pseudo mask dirtectly used in training segmentation network?if not, can you tell how to generate it? Thank you!

Hi, your work is superb. can i know whether obtain_CAM_masking.py takes a lot of time to gets completed ? it seems there are two loops in GradCAM process. i have 103 classes.

Hi thanks for sharing a great work!

In the previous issue, you said you used the CCT's code for semi-supervised learning with the replacement of pseudo labels. Does that mean you used the default configuration provided in CCT?