Comments (4)
I believe that this is where they can be downloaded but confirmation would be helpful:
https://www-i6.informatik.rwth-aachen.de/imageclef/resources/saiaprtc12/
from groundinglmm.
Same question. There doesn't seem to be a download link
from groundinglmm.
I believe that this is where they can be downloaded but confirmation would be helpful: https://www-i6.informatik.rwth-aachen.de/imageclef/resources/saiaprtc12/
Thanks! "Referring Expression Segmentation" may not require it; I was able to correctly evaluate on RefCoco without it.
from groundinglmm.
Hi @yliu-cs and @LarsLiden,
Thank you both for your interest in our work and for reaching out with your questions.
To clarify, for the RefCLEF portion of our project, the images from the saiapr_tc-12 dataset are indeed necessary. You can download these images from the following link: saiapr_tc-12 dataset.
Regarding the training process for referring expression segmentation, as outlined in our training doc, the model is primarily fine-tuned using the RefCOCO, RefCOCO+, and RefCOCOg datasets. The RefCLEF (saiapr_tc-12) dataset, is utilized for the demo model which uses a mixture of open-source datasets.
I hope this clears up any confusion. If you have any more questions or need further assistance, feel free to ask.
from groundinglmm.
Related Issues (20)
- About region caption HOT 2
- may i ask your total parameter?
- Some bugs in the GranD_ReferringSegm_ds.py
- Online Demo Down HOT 1
- Fine-tuning Grounded Conversation Generation (GCG) Task HOT 4
- token_positives HOT 2
- assertion error cur_len == total_len HOT 1
- can not install mmcv HOT 2
- Can not find file for glamm_conda_env.zip in the given Google Drive Link HOT 4
- Training on New Data HOT 2
- training V-L and L-P projection layer HOT 1
- Can not download the train.json file for visual genome
- How can I let the model receive multiple images at once HOT 1
- How should I train on the GranD dataset
- How can I finetune on combined tasks?
- Confusing referring segmentation results. HOT 1
- mmcv failed to install HOT 1
- AssertionError when running a demo
- Offline demo error
- Why is it that during the computation of segmentation results, the model() function is used instead of model.generate()? Wouldn't this mean that when predicting the next token, the information viewed is from the actual token rather than the predicted one?
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