---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- video-caption-evaluation
- reference-free
- factual-analysis
- vision-language
library_name: transformers
base_model: Qwen/Qwen2.5-VL-3B-Instruct
datasets:
- dipta007/ActivityNet-FG-It
arxiv: 2509.16538
---

# VC-Inspector-3B

<a href="https://arxiv.org/abs/2509.16538" target="_blank">
    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2509.16538-b31b1b.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/collections/dipta007/vc-inspector" target="_blank">
    <img alt="Models" src="https://img.shields.io/badge/HuggingFace-Models-orange" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/datasets/dipta007/ActivityNet-FG-It" target="_blank">
    <img alt="Dataset" src="https://img.shields.io/badge/HuggingFace-Dataset-blue" style="display: inline-block; vertical-align: middle;"/>
</a>

## Introduction

**VC-Inspector-3B** is a lightweight, open-source large multimodal model (LMM) for **reference-free evaluation of video captions** with a focus on **factual accuracy**. This is the smaller, more efficient variant of VC-Inspector, ideal for resource-constrained environments while still achieving strong performance.

Unlike existing metrics that suffer from limited context handling, weak factuality assessment, or reliance on proprietary services, VC-Inspector offers a reproducible, fact-aware alternative that aligns closely with human judgments.

This model is fine-tuned from [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) using LoRA on our synthetic dataset [ActivityNet-FG-It](https://huggingface.co/datasets/dipta007/ActivityNet-FG-It), which contains 44K video-caption pairs with controlled factual errors and quality annotations.

### Key Features

- **Lightweight**: Only 3B parameters - suitable for on-device or resource-constrained deployment
- **Reference-free Evaluation**: Evaluates video captions without requiring ground-truth references
- **Factual Grounding**: Detects factual errors in objects and actions within captions
- **Interpretable Outputs**: Generates quality scores (1-5) with natural language explanations
- **Cross-domain Generalization**: Works on both video and image caption evaluation
- **Fast Inference**: 0.30 seconds per video clip on A100 GPU

### Model Architecture

VC-Inspector-3B is built on Qwen2.5-VL-3B-Instruct with the following modifications:
- **Vision Encoder**: Frozen (preserves generalization)
- **Visual-Language Projector**: Frozen
- **LLM Component**: Fine-tuned with LoRA (rank=32, alpha=32)

## Evaluation Results

### Correlation with Human Judgments on VATEX-Eval

| Metric | Type | Kendall's τ_b | Spearman's ρ |
|:-------|:-----|:-------------:|:------------:|
| EMScore | Reference-free | 22.88 | 29.79 |
| CLIPScore | Reference-free | 22.33 | 29.09 |
| ViCLIPScore | Reference-free | 30.92 | 39.86 |
| Qwen2.5-VL-3B (base) | Reference-free | 31.29 | 36.43 |
| G-VEval (GPT-4o) | Reference-free | 39.40 | - |
| **VC-Inspector-3B** | Reference-free | **37.99** | **42.45** |

### Cross-domain Evaluation on Image Caption Benchmarks

| Metric | Flickr8K-Expert (τ_b) | Flickr8K-CF (τ_b) |
|:-------|:---------------------:|:-----------------:|
| CLIPScore (ref-free) | 51.10 | 34.40 |
| PAC-S (ref-free) | 53.90 | 36.00 |
| **VC-Inspector-3B** | **59.86** | **39.00** |

### Synthetic Dataset Evaluation

| Dataset | Kendall's τ_b | Spearman's ρ |
|:--------|:-------------:|:------------:|
| ActivityNet-FG-Eval | 49.53 | 62.01 |
| YouCook2-FG-Eval | 44.29 | 55.31 |

### Computational Efficiency

| Metric | Time per clip (A100) |
|:-------|:--------------------:|
| EMScore | 0.42s |
| ViCLIPScore | 0.34s |
| **VC-Inspector-3B** | **0.30s** |

## Requirements

```bash
pip install torch transformers accelerate
pip install qwen-vl-utils[decord]==0.0.8
pip install flash-attn --no-build-isolation
```

## Quickstart

### Using Transformers

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

# Load model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "dipta007/VCInspector-3B",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("dipta007/VCInspector-3B")

# Prepare input
caption = "A man is playing guitar in a field"
prompt = f"""<caption>{caption}</caption>

You are given a video and a caption describing the video content. Please rate the helpfulness, relevance, accuracy, level of details of the caption. The overall score should be on a scale of 1 to 5, where a higher score indicates better overall performance. Please first output a single line containing only one integer indicating the score. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias. STRICTLY FOLLOW THE FORMAT."""

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "path/to/video.mp4", "max_pixels": 360 * 420, "fps": 1.0},
            {"type": "text", "text": prompt},
        ],
    }
]

# Process and generate
text = 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=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
    **video_kwargs,
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```

### Example Output

```
4
The caption does not accurately capture the video content. For example, the objects (guitar) are incorrect.
```

### Using with ms-swift (vLLM backend)

```python
from swift.llm import VllmEngine, InferRequest, RequestConfig
import os

os.environ["VIDEO_MAX_PIXELS"] = "50176"
os.environ["FPS_MAX_FRAMES"] = "12"

engine = VllmEngine(
    "dipta007/VCInspector-3B",
    max_model_len=32768,
    limit_mm_per_prompt={"image": 32}
)

# Prepare request
request = InferRequest(
    messages=[{"role": "user", "content": f"<image>\n{prompt}"}],
    images=["frame1.jpg", "frame2.jpg", ...]  # Video frames
)
config = RequestConfig(max_tokens=256, temperature=0.0)
response = engine.infer([request], config)
print(response[0].choices[0].message.content)
```

## Output Format

VC-Inspector outputs two components:

1. **Quality Score** (Line 1): Integer from 1-5
   - 5: Caption is accurate and comprehensive
   - 4: Minor factual errors
   - 3: Moderate factual errors
   - 2: Significant factual errors
   - 1: Major factual errors or completely incorrect

2. **Explanation** (Line 2+): Natural language explanation identifying:
   - Incorrect objects (e.g., "guitar" instead of "violin")
   - Incorrect actions (e.g., "running" instead of "walking")

## Training Details

| Hyperparameter | Value |
|:---------------|:------|
| Base Model | Qwen2.5-VL-3B-Instruct |
| Training Data | ActivityNet-FG-It (44K samples) |
| Epochs | 1 |
| Global Batch Size | 128 |
| Learning Rate | 1e-4 |
| LR Scheduler | Cosine (min: 1e-5) |
| LoRA Rank | 32 |
| LoRA Alpha | 32 |
| LoRA Dropout | 0.05 |
| Number of Frames | 32 |
| Training Time | ~32 GPU hours (A100) |

## Ablation Studies

### Impact of Explanation Supervision

| Setting | Kendall's τ_b | Spearman's ρ |
|:--------|:-------------:|:------------:|
| Without Explanations | 34.29 | 38.18 |
| **With Explanations** | **37.99** | **42.45** |

### Data Synthesis Strategy

| Strategy | Kendall's τ_b | Spearman's ρ |
|:---------|:-------------:|:------------:|
| Change objects only | 36.40 | 41.20 |
| Change actions only | 33.23 | 39.63 |
| **Change both (Ours)** | **37.99** | **42.45** |

## When to Use VC-Inspector-3B vs 7B

| Use Case | Recommended Model |
|:---------|:------------------|
| Resource-constrained environments | **3B** |
| On-device deployment | **3B** |
| Batch processing large datasets | **3B** |
| Maximum accuracy required | 7B |
| Research benchmarking | 7B |

## Limitations

- Primarily targets object and action correctness; attributes, spatial relationships, and fine-grained temporal ordering are not explicitly modeled
- Training relies on synthetically generated captions and pseudo-scores
- Slightly lower performance than the 7B variant on challenging cases

## Citation

If you find this work useful, please cite our paper:

```bibtex
@misc{dipta2025advancingreferencefreeevaluationvideo,
      title={Advancing Reference-free Evaluation of Video Captions with Factual Analysis},
      author={Shubhashis Roy Dipta and Tz-Ying Wu and Subarna Tripathi},
      year={2025},
      eprint={2509.16538},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.16538},
}
```

## Acknowledgements

This work builds upon [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) and uses [ms-swift](https://github.com/modelscope/ms-swift) for training.