jialinwu17 / self_critical_vqa Goto Github PK

Following after the the instructions in the readme file and using the first two pre-trained networks does not reproduce the 49.5% accuracy reported in the paper.
Also, following the experimental settings in the paper doesn't reproduce the results.
Tried 20 different seeds and the best score is 49.

Can you please share the exact settings you use to get 49.5?

Thanks!

https://drive.google.com/drive/folders/1IXTsTudZtYLqmKzsXxIZbXfCnys_Izxr

Because the file is larger than 20G , so google drive can not download in China. Could scholar provide another download link for it?
I want to reproduce the result. Could you help me?

I am getting this error:
FileNotFoundError: [Errno 2] No such file or directory: 'train36_imgid2img.pkl'

I am trying to recreate the glove embeddings for a different set of objects and attributes. Would you be able to share the code for creating the glove6b_init_objects_300d.npy and glove6b_init_attributes_300d.npy files? Thank you!

It seems that I don't have permission to access this resource.

Can you provide these two files again or the method to generate them? The previous link shared is no longer working, and I really need these two files or the script to generate them. Thank you very much！

Would it be possible to provide trainval_ans2label.pkl and trainval_label2ans.pkl files. The files generated from compute_softscore.py does not seem to be consistent with the labels in VQA_caption_{name}dataset.pkl files.

For instance, for qid 166207000, the GT answers should be robot/beep*, but the label returned by the dataset is 142, which corresponds to the answer: 'real' in label2ans.

like baiduYunpan or others available in China.
Thank you.

Hi, jialing!
Recently, I am trying to introduce 'the most influential objects' into my model. However, when I check 'create_vqx_hint.py', I don't understand the code at lines 187-189.

if cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1]) > 0.3 :
        if hint_score_attr[j] <= cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1]):
                hint_score[j] = cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1])

I also found that you don't use 'hint_score_attr' anymore, but this code would change 'hint_score[j]' that has been assigned at line 179. Thus, is it should be more reasonable that using 'hint_score_attr[j]' to replace 'hint_score[j]' at line 189. like this:

if cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1]) > 0.3 :
        if hint_score_attr[j] <= cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1]):
                hint_score_attr[j] = cosine_similarity(exp_emb[attr_token:attr_token+1], atts[j:j+1])