metadata
license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
base_model: Qwen/Qwen3-VL-2B-Instruct
tags:
- video
- temporal-grounding
- qwen3-vl
TimeLens2-2B
TimeLens2-2B is a compact video multimodal large language model specialized for temporal grounding. Given a video and a text query, it returns all time intervals containing the relevant visual evidence.
The model is built on Qwen3-VL-2B-Instruct and achieves 44.5 average mIoU across seven temporal grounding benchmarks.
TimeLens2-2B sets a new state of the art at the 2B scale, outperforming all three size-matched baselines on every benchmark and even surpassing TimeLens-8B by 2.4 mIoU on average.
Paper
TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
Benchmark Results
Inference
pip install -U torch torchvision "transformers>=4.57.0" accelerate "qwen-vl-utils[decord]>=0.0.14"
pip install -U flash-attn --no-build-isolation
from pathlib import Path
from qwen_vl_utils import process_vision_info
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "MCG-NJU/TimeLens2-2B"
video_path = "/path/to/video.mp4"
query = "A man opens the refrigerator."
model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
attn_implementation="flash_attention_2",
)
processor = AutoProcessor.from_pretrained(model_id)
prompt = (
f'Given the query: "{query}", return ALL time spans (in seconds) where the query is relevant.\n'
"Output format MUST be a JSON array of [start, end] pairs.\n"
)
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": Path(video_path).resolve().as_uri(),
"fps": 2.0,
"min_pixels": 32 * 32,
"max_pixels": 480 * 480,
"total_pixels": 128000 * 32 * 32,
},
{"type": "text", "text": prompt},
],
}
]
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
images, videos, video_kwargs = process_vision_info(
messages,
image_patch_size=16,
return_video_kwargs=True,
return_video_metadata=True,
)
if videos is not None:
videos, video_metadatas = zip(*videos)
videos, video_metadatas = list(videos), list(video_metadatas)
else:
video_metadatas = None
inputs = processor(
text=text,
images=images,
videos=videos,
video_metadata=video_metadatas,
do_resize=False,
return_tensors="pt",
**video_kwargs,
).to(model.device)
output_ids = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.01,
top_p=0.001,
top_k=1,
repetition_penalty=1.0,
)
output_ids = [
output[len(input_ids) :]
for input_ids, output in zip(inputs.input_ids, output_ids)
]
response = processor.batch_decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)
print(response[0])
Citation
@misc{zhu2026timelens2,
title={TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs},
author={Yuhan Zhu and Changlian Ma and Xiangyu Zeng and Xinhao Li and Zhiqiu Zhang and Songze Li and Jun Zhang and Tianxiang Jiang and Yuandong Yang and Ziang Yan and Zikang Wang and Xinyu Chen and Haoran Chen and Shaowei Zhang and Limin Wang},
year={2026},
eprint={2607.17423},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2607.17423},
}