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Model Details
Model Provider
aimagelab
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationmodel_id = "aimagelab/CounterVid-Qwen2.5-VL-7B-LoRA"model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",).eval()processor = AutoProcessor.from_pretrained(model_id)
For complete video inference and evaluation examples, see the CounterVid repository and project page.
Qwen/Qwen2.5-VL-7B-InstructThis is a research model for video-language understanding, with a focus on action recognition and temporal reasoning. It inherits the capabilities, limitations, biases, and license conditions of its base model. It may still produce incorrect or unsupported answers and should not be used as the sole basis for high-stakes decisions.
bibtex
@inproceedings{poppi2026countervid,title = {{CounterVid}: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models},author = {Poppi, Tobia and Uzkent, Burak and Garg, Amanmeet and Porto, Lucas and Kessler, Garin and Yang, Yezhou and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita and Schiffers, Florian},booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},year = {2026}}
This work was conducted during Tobia Poppi's Applied Scientist internship with the Amazon Prime Video team in Seattle.