MrFrogIsMe/vertag-fade

FADE checkpoints (VERTAG, ACCV 2026)

FADE (Faithful Additive DEscriptor) is the examiner-confusion retriever of VERTAG (ACCV 2026). A light additive attention-pooling head on a mostly frozen backbone pools an image's patch tokens $v_i$ into $d=\sum_i a_i v_i$, so the cosine between two trademarks decomposes exactly into patch-pair contributions $C_{ij}$ that sum to the retrieval score itself.

File	Backbone	Use it for	G1 R@100
fade_siglip_so400m.safetensors	SigLIP-SO400M/14, 224 px	retrieval: the strongest retriever in the paper (Tab. 3)	0.609
fade_dinov2_vitl14_reg.safetensors	DINOv2-L/14 with registers, 224 px	the model analysed in the paper (Tab. 2, Fig. 3) and the source of the explainer's region evidence	0.146

Usage

The checkpoints are loaded by the code in the VERTAG repository.

git clone https://github.com/spaces-lalala/VERTAG.git
cd VERTAG/FADE
pip install -r requirements.txt
hf download MrFrogIsMe/vertag-fade --include "*.safetensors" --local-dir checkpoints

python explain.py --checkpoint checkpoints/fade_dinov2_vitl14_reg.safetensors \
    --applied applied.jpg --cited cited.jpg
python retrieve.py --checkpoint checkpoints/fade_siglip_so400m.safetensors \
    --gallery /path/to/gallery_dir --query query.jpg --explain 3

A file stores only the tensors training changed; the rest of the backbone is downloaded from its official pretrained weights on first use (DINOv2 from torch.hub, SigLIP from Hugging Face).

Model card

• Source. These are the paper's models, not retrained ones. Each file is a slim export of the paper's checkpoint, and every tensor was checked against the original checkpoint when it was exported.
• Architecture. Only the last two transformer blocks, the backbone's final norm and the FADE head were trained.
• Training data. METU-v2 copy-detection pairs (its 417 queries excluded) mixed with examiner pairs (applied mark → cited mark) from TIPO office actions published up to 2023. The benchmark queries are the office actions after 2023. Evaluation on METU-v2 is therefore not zero-shot. Some prior marks cited against benchmark queries also occur in the training pairs; the paper reports the effect (Suppl. S1).
• Intended use. Research on trademark retrieval and its explanation. A similarity score or a $C_{ij}$ map is not a legal assessment of likelihood of confusion.

File	SHA256
fade_dinov2_vitl14_reg.safetensors	41702efeaa7ec5dc2350173996aefd6ef2805ef063e2af45f72417dcc3eadce6
fade_siglip_so400m.safetensors	d03b6aa280503a20947d8762dca2a1dbd946a54bdd2eef2c9515f0881e0a1a05

License

The weights are released under CC BY-NC 4.0; commercial use is prohibited. They are derived from DINOv2 and SigLIP, both Apache-2.0 (LICENSE-APACHE-2.0.txt).

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}
}