MrFrogIsMe/vertag-explainer-lora

explainer LoRA (VERTAG, ACCV 2026)

A LoRA adapter for Qwen/Qwen2.5-VL-7B-Instruct: given an applied trademark and the prior mark it was refused over, it writes an examiner-style rationale (Traditional Chinese) for why the two marks are similar, item by item over the visual likelihood-of-confusion factors. It is the grounded-explanation module of VERTAG (ACCV 2026). The code, the exact prompt and both adapters are described in `explainer/`.

The default adapter. It was trained with the visual-only prompt that generate.py runs, and it is the adapter of the paper's registration-number grounding (Tab. 4, registration-number row: fabricated registration numbers fall from 100% to 5% at no cost in content).

Usage

git clone https://github.com/spaces-lalala/VERTAG.git
cd VERTAG/explainer
pip install -r requirements.txt     # GPU with ~17 GB free (bf16)
python generate.py --applied applied.jpg --cited cited.jpg --regno 00820591 \
    --condition C --regno-evidence --adapter MrFrogIsMe/vertag-explainer-lora

--adapter takes this Hugging Face id directly; omit it to run the base model zero-shot.

Model card

• —Source. The adapter files used in the paper, not retrained ones.
• —Architecture. LoRA (r = 16, α = 32) on the q/k/v/o projections of the language model of Qwen/Qwen2.5-VL-7B-Instruct; the vision tower is unchanged. Applied to the base model in bf16.
• —Training data. 6,000 applied → cited pairs from TIPO office actions published up to 2023, with the examiner's similarity paragraph as the target; QLoRA (4-bit), 2 epochs. The paper's evaluation cases are office actions after 2023.
• —Limitations. Without the cited registration number (--regno-evidence), a fine-tuned adapter almost always writes a fabricated one (paper Tab. 4: 100%). The output is automatically generated research text, not an examination opinion of TIPO and not legal advice.
• —Prompt. Trained with the prompt it is run with.

License

The adapter is released under CC BY-NC 4.0; commercial use is prohibited. It is applied to Qwen/Qwen2.5-VL-7B-Instruct (Apache-2.0, LICENSE-APACHE-2.0.txt), whose terms also apply.

Citation

@inproceedings{yen2026vertag,
  title     = {{VERTAG}: Visual Examiner Rationales for Trademarks with Atomic Grounding --- A Confusion Benchmark, Faithful Retriever, and Explanation-Coverage Metric},
  author    = {Yen, Sheng-Yuan and Chou, Chia-Yi and Peng, Chi-Tse and Ye, Chian-Yu and Yu, Tsan-Wei and Ko, Chih-Chun and Wu, Yi-Chieh},
  booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
  year      = {2026}
}