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HugC
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Model Details
Model Provider
HugC
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Results on MMEB (Cls / VQA / Ret / Grd) and MVEB (ID-Rec / Re-ID / ID-Grd / ID-Edit). Metrics are reported as in the paper (average Precision@1 across datasets within each task group).
VisME is trained on a mixture of:
| Benchmark | Role |
|---|---|
| MMEB-v1 | General multimodal embedding tasks (classification, VQA, retrieval, grounding) |
| MVEB | Visual-identity retrieval across recognition, re-ID, grounding, and editing |
The MVEB datasets are now open-sourced. Training code will be released soon.
python
import torchimport torch.nn.functional as Ffrom io import BytesIOfrom urllib.request import Request, urlopenfrom PIL import Imagefrom visme import VisME model = VisME("path/to/this/repo")model = model.cuda().eval() instruction = "Represent the face with the following text."text = "Retrieve all images with the same cartoon character." samples = { "SpiderMan_comic_E616": "https://static.wikia.nocookie.net/spiderman/images/a/ad/Peter_Parker_%28Earth-616%29_017.png/revision/latest?cb=20210807043502", "SpiderMan_promo_E199999": "https://static.wikia.nocookie.net/marveldatabase/images/2/28/Peter_Parker_%28Earth-199999%29_from_Spider-Man_No_Way_Home_promotional_art_002.jpg/revision/latest/scale-to-width-down/1000?cb=20230730084204", "Toxin": "https://static.wikia.nocookie.net/superheroes/images/5/53/Toxin.jpg/revision/latest?cb=20240813171932",} def load_image(url: str) -> Image.Image: req = Request(url, headers={"User-Agent": "Mozilla/5.0"}) with urlopen(req, timeout=30) as resp: return Image.open(BytesIO(resp.read())).convert("RGB") names = list(samples.keys())batch = [ {"image": load_image(samples[name]), "text": text, "instruction": instruction} for name in names] with torch.no_grad(): embeddings = model.encode_input(batch) # shape: (3, 3584), L2-normalized # Pairwise cosine similarity# SpiderMan_comic_E616 <-> SpiderMan_promo_E199999# SpiderMan_comic_E616 <-> Toxin# SpiderMan_promo_E199999 <-> Toxinfor i in range(len(names)): for j in range(i + 1, len(names)): sim = F.cosine_similarity(embeddings[i : i + 1], embeddings[j : j + 1]).item() print(f"cosine_similarity({names[i]}, {names[j]}) = {sim:.4f}")Benchmark evaluation code for MMEB and MVEB is coming soon. In the meantime, please refer to the paper for full experimental settings and baselines.
If you find VisME or MVEB useful, please cite:
bibtex
@inproceedings{cao2026illuminating, title={Illuminating Visual Identity in Universal Multimodal Embeddings}, author={Cao, Jiawei and Feng, Junyi and Hua, Jiashen and Huang, Ziheng and Deng, Bing and Wu, Kaijie and Gu, Chaochen and Ye, Jieping}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={8737--8748}, year={2026}}This model is built upon Qwen2.5-VL-7B-Instruct. Please follow the license terms of the base model and the respective datasets used during training.
import torchimport torch.nn.functional as Ffrom io import BytesIOfrom urllib.request import Request, urlopenfrom PIL import Imagefrom visme import VisME model = VisME("path/to/this/repo")model = model.cuda().eval() instruction = "Represent the face with the following text."text = "Retrieve all images with the same cartoon character." samples = { "SpiderMan_comic_E616": "https://static.wikia.nocookie.net/spiderman/images/a/ad/Peter_Parker_%28Earth-616%29_017.png/revision/latest?cb=20210807043502", "SpiderMan_promo_E199999": "https://static.wikia.nocookie.net/marveldatabase/images/2/28/Peter_Parker_%28Earth-199999%29_from_Spider-Man_No_Way_Home_promotional_art_002.jpg/revision/latest/scale-to-width-down/1000?cb=20230730084204", "Toxin": "https://static.wikia.nocookie.net/superheroes/images/5/53/Toxin.jpg/revision/latest?cb=20240813171932",} def load_image(url: str) -> Image.Image: req = Request(url, headers={"User-Agent": "Mozilla/5.0"}) with urlopen(req, timeout=30) as resp: return Image.open(BytesIO(resp.read())).convert("RGB") names = list(samples.keys())batch = [ {"image": load_image(samples[name]), "text": text, "instruction": instruction} for name in names] with torch.no_grad(): embeddings = model.encode_input(batch) # shape: (3, 3584), L2-normalized # Pairwise cosine similarity# SpiderMan_comic_E616 <-> SpiderMan_promo_E199999# SpiderMan_comic_E616 <-> Toxin# SpiderMan_promo_E199999 <-> Toxinfor i in range(len(names)): for j in range(i + 1, len(names)): sim = F.cosine_similarity(embeddings[i : i + 1], embeddings[j : j + 1]).item() print(f"cosine_similarity({names[i]}, {names[j]}) = {sim:.4f}")@inproceedings{cao2026illuminating, title={Illuminating Visual Identity in Universal Multimodal Embeddings}, author={Cao, Jiawei and Feng, Junyi and Hua, Jiashen and Huang, Ziheng and Deng, Bing and Wu, Kaijie and Gu, Chaochen and Ye, Jieping}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={8737--8748}, year={2026}}