🎯 General Video Embedder (GVE)

One Embedder for All Video Retrieval Scenarios
Queries of text, image, video, or any combination modalities — GVE understands them all for representations, zero-shot, without in-domain training.

GVE is the first video embedding model that generalizes across 9 abilities, including 3 diverse retrieval tasks and 6 domains — from coarse text-to-video to fine-grained spatial/temporal queries, composed (text+image) queries, and long-context retrieval — all evaluated on our new Universal Video Retrieval Benchmark (UVRB).

Built on Qwen2.5-VL and trained only with LoRA with 13M collected and synthesized multimodal data, GVE achieves SOTA zero-shot performance than competitors.

🌟 Why GVE?

Capability	Existing Works	GVE
Query Flexibility	Only text	✅ Text, ✅ Image, ✅ Video, ✅ Text+Image, ✅ Text+Video
Fine-grained Understanding	Weak on spatial-temporal details	S: 0.821, T: 0.469 (SOTA)
Training Data	Uses in-domain test data (e.g., MSRVTT)	Synthesized data — true zero-shot
Performance	Unite-7B (8.3B): 55.9	GVE-3B (3.8B): 0.571 → better with half the size; GVE-7B (3.8B): 0.600

📊 Performance on UVRB

TXT: Textual Video Retrieval
CMP: Composed Video Retrieval
VIS: Visual Video Retrieval
CG: Coarse-grained Video Retrieval
FG: Fine-grained Video Retrieval
LC: Long-Context Video Retrieval
S: Spatial Video Retrieval
T: Temporal Video Retrieval
PR: Partially Relevant Video Retrieval

For each column: highest score is bolded, second-highest is underlined.

Model	AVG	TXT	CMP	VIS	CG	FG	LC	S	T	PR
CLIP4Clip	0.416	0.401	0.178	0.714	0.380	0.360	0.463	0.559	0.285	0.236
ViCLIP	0.375	0.336	0.263	0.640	0.380	0.315	0.313	0.484	0.289	0.171
VideoCLIP-XL	0.510	0.550	0.227	0.632	0.558	0.493	0.600	0.787	0.381	0.310
LanguageBind	0.508	0.543	0.231	0.645	0.539	0.479	0.610	0.723	0.378	0.336
InternVideo2-1B	0.420	0.422	0.248	0.581	0.480	0.403	0.383	0.606	0.413	0.189
InternVideo2-6B	0.445	0.448	0.220	0.660	0.504	0.417	0.423	0.631	0.400	0.220
GME-2B	0.416	0.539	0.345	0.597	0.461	0.471	0.685	0.716	0.349	0.347
Unite-2B	0.507	0.536	0.242	0.654	0.455	0.471	0.681	0.725	0.347	0.341
VLM2Vec-V2	0.538	0.587	0.263	0.613	0.498	0.502	0.762	0.809	0.348	0.348
BGE-VL	0.480	0.497	0.268	0.622	0.448	0.406	0.636	0.664	0.292	0.261
UniME-7B	0.542	0.561	0.308	0.702	0.500	0.518	0.664	0.785	0.396	0.373
B3-7B	0.538	0.570	0.270	0.678	0.482	0.505	0.722	0.797	0.364	0.355
GME-7B	0.562	0.604	0.341	0.615	0.518	0.507	0.788	0.749	0.373	0.398
Unite-7B	0.559	0.609	0.254	0.666	0.541	0.539	0.746	0.779	0.412	0.425
GVE-3B	0.571	0.619	0.304	0.647	0.552	0.541	0.764	0.816	0.430	0.377
GVE-7B	0.600	0.657	0.312	0.657	0.587	0.570	0.814	0.821	0.469	0.419

🚀 Get Started

Loading model

model_path = 'Alibaba-NLP/GVE-7B'
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map='auto', low_cpu_mem_usage=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = 'left'

Processing inputs

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "./asset/video_example.mp4",
                "max_pixels": 200 * 28 * 28,
                "fps": 1.0,
                "max_frames": 8,
            },
            {"type": "text", "text": "Describe this video."},
        ],
    }
]
texts = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
    text=[texts],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    truncation=True,
    max_length=1200,
    return_tensors="pt",
    **video_kwargs,
).to("cuda")

Embedding

outputs = model(**inputs)
embedding = F.normalize(outputs['last_hidden_state'][:, -1, :], p=2, dim=1)

📚 Citation

@misc{guo2025gve,
  title={Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum}, 
  author={Zhuoning Guo and Mingxin Li and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Xiaowen Chu},
  year={2025},
  eprint={2510.27571},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2510.27571}, 
}