JasonQSY
commented
3 weeks ago For these data you'll really need to re-process the dataset. To make it possible I've released some data processing and baseline code. https://github.com/JasonQSY/AffordanceLLM I'm sorry I'm not able to debug to make sure the code is easy to run. I've graduated recently and lost a lot of access to specific machines. If you find any issues I'll appreciate a PR. If you plan to release your implementation of AffordanceLLM in the future I'm happy to put it on the project website and acknowledge your contribution.
Fully-supervised setting:
- Hard split: You're right.
- Easy split: set
obj_list=locate_seen_obj_listin https://github.com/JasonQSY/AffordanceLLM/blob/main/data_processing/build_llava_agd20k.py#L133 andobj_list=locate_unseen_obj_listin https://github.com/JasonQSY/AffordanceLLM/blob/main/data_processing/build_llava_agd20k.py#L145C11-L145C42 run the script to generate the json file.
Weakly-supervised setting:
- Easy split: It's LOCATE unseen. Set
--divide=Unseen. - Hard split: Take a look at the code here and set
--divide=Generalization.
Apologize for the questions about your another significant work... Since I have no way to contact you separately, I am posting here after seeing the related issue.
I am interested in your another paper AffordanceLLM: Grounding Affordance from Vision Language Models and am currently working on its implementation.
Thankfully, i was able to download the hard split of the benchmark. But I wonder how to generate the Easy and hard split data.
The following part of the paper: Easy split
Hard split
Could you please tell me detail about the weakly supervised method part? (which images are you using and so on...) And if you use data from a weakly supervised method, how did you get the GT data needed for affordance prediction and learning?