Comments (3)
Thank you for your interest in our work. StructuralLM and LayoutLM propose different solutions to the problem of visually-rich document modeling. The StructuralLM approach is original and distinct from LayoutLM in document types, model architectures and research findings. Our StructuralLM paper has properly cited the previous work of LayoutLM(v1), and referred to the baseline models used in the v1 paper, including the name RoBERTa. LayoutLM(v2) later replaced the baseline from RoBERTa to UniLMv2, which led to the discrepancy in the model names. As StructuralLM and LayoutLM both address the same research problem, which is an emerging topic with limited studies in the past, that results in the similarity in citations of the related work sections.
We sincerely appreciate the contribution of LayoutLM to this research field.
from alicemind.
Both accepted by ACL2021, who copy who
from alicemind.
Thank you for your interest in our work. StructuralLM and LayoutLM propose different solutions to the problem of visually-rich document modeling. The StructuralLM approach is original and distinct from LayoutLM in document types, model architectures and research findings. Our StructuralLM paper has properly cited the previous work of LayoutLM(v1), and referred to the baseline models used in the v1 paper, including the name RoBERTa. LayoutLM(v2) later replaced the baseline from RoBERTa to UniLMv2, which led to the discrepancy in the model names. As StructuralLM and LayoutLM both address the same research problem, which is an emerging topic with limited studies in the past, that results in the similarity in citations of the related work sections. We sincerely appreciate the contribution of LayoutLM to this research field.
interesting
from alicemind.
Related Issues (20)
- When the mPLUG-2 model can be released? HOT 2
- Fairness of SOTA comparison in mPLUG-2 HOT 2
- RuntimeError: gather(): Expected dtype int64 for index
- There might be sth wrong in this file mPLUG/videocap_mplug.py
- Fine-tuning video captions HOT 1
- Inference of image captioning on single image HOT 4
- how to get the pre-trained model "ViT-L-14.tar"
- how to get ued model A Unified Pretraining Framework For Passage Ranking And Expansion
- “mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video”代码是否会开源?
- Grounding checkpoint evaluation results
- “VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning”代码是否会开源?
- Missing partial code and files of gqa for VQA in mPLUG
- Zero-Shot Video Captioning script issues HOT 1
- Could you upload StructuralLM to HuggingFace ?
- The logprob of image captioning result of mplug is very small
- SDCUP
- SDCUP训练问题
- 表格数据集
- mplug中两处代码错误问题
- can i pretrain mPLUG model with my own dataset
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