README.md

---
license: apache-2.0
tags:
- pytorch
---

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<div align="center">
  <h1>🚀 ViSAGE @ CVPR-NTIRE Video Saliency Prediction Challenge 2026</h1>

  <p>
    <b>Kun Wang</b><sup>1</sup>&nbsp;
    <b>Yupeng Hu</b><sup>1</sup>&nbsp;
    <b>Zhiran Li</b><sup>1</sup>&nbsp;
    <b>Hao Liu</b><sup>1</sup>&nbsp;
    <b>Qianlong Xiang</b><sup>2,3,4</sup>&nbsp;
    <b>Liqiang Nie</b><sup>2</sup>
  </p>

  <p>
    <sup>1</sup>Shandong University<br>
    <sup>2</sup>Harbin Institute of Technology<br>
    <sup>3</sup>City University of Hong Kong<br>
    <sup>4</sup>Shenzhen Loop Area Institute
  </p>
</div>

These are the official implementation, pre-trained model weights, and configuration files for **ViSAGE**, designed for the NTIRE 2026 Challenge on Video Saliency Prediction (CVPRW 2026).

🔗 **Paper:** [Accepted by CVPRW 2026](https://arxiv.org)
🔗 **GitHub Repository:** [iLearn-Lab/CVPRW26-ViSAGE](https://github.com/iLearn-Lab/CVPRW26-ViSAGE.git)
🔗 **Challenge Page:** [NTIRE 2026 VSP Challenge](https://www.codabench.org/competitions/12842/)

---

<p align="center">
  <video src="https://github.com/user-attachments/assets/a2dbabc0-9d8e-4f7a-8b16-c2d56af7b071" controls width="95%"></video>
</p>

---

## 📌 Model Information

### 1. Model Name
**ViSAGE(Video Saliency with Adaptive Gated Experts)**

### 2. Task Type & Applicable Tasks
- **Task Type:** Video Saliency Prediction (VSP) / Computer Vision
- **Applicable Tasks:** Robust and adaptive prediction of human visual attention (saliency maps) in dynamic video sequences.

### 3. Project Introduction
Video Saliency Prediction requires capturing complex spatio-temporal dynamics and human visual priors. **ViSAGE** tackles this by leveraging a powerful multi-expert ensemble framework.

> 💡 **Method Highlight:** The framework consists of a shared **InternVideo2 backbone** adapted via two-stage LoRA fine-tuning, alongside dual specialized experts utilizing Temporal Modulation (for explicit spatial priors) and Multi-Scale Fusion (for adaptive data-driven perception). For robust performance, the **Ensemble Fusion Module** obtains the final prediction by converting the expert outputs to logit space before averaging, which provides significantly more accurate estimation than simple saliency map averaging.

### 4. Training Data Source
- Dataset provided by the **NTIRE 2026 Video Saliency Prediction Challenge** (Private Test and Validation sets).

---

## 🚀 Usage & Basic Inference

### Step 1: Prepare the Environment
Clone the GitHub repository and set up the Conda environment:
```bash
git clone https://github.com/iLearn-Lab/CVPRW26-ViSAGE.git
cd ViSAGE
```
```bash
conda create -n visage python=3.10 -y
conda activate visage
pip install -r requirements.txt
```

### Step 2: Data & Pre-trained Weights Preparation
1. **Challenge Data:** Use the provided scripts to extract frames from the source videos. The extracted frames will be automatically saved to `derived_fullfps`.
   *(⚠️ **Important:** Do not modify the output directory name `derived_fullfps` unless you manually update the path configs in all inference scripts.)*
  ```bash
   python video_to_frames.py
  ```
2. **ViSAGE Checkpoints:** Download our model checkpoints(https://huggingface.co/iLearn-Lab/CVPRW26-ViSAGE).
3. **InternVideo2 Backbone:** Download the pre-trained `InternVideo2-Stage2_6B-224p-f4` model from [Hugging Face](https://huggingface.co/OpenGVLab/InternVideo2-Stage2_6B-224p-f4) and clone the `InternVideo` repo:
  ```bash
   git clone https://github.com/OpenGVLab/InternVideo.git
   *(Update the pre-trained weight paths in `Expert1/inference.py` and `Expert2/inference.py` to match your local directory).*
  ```
### Step 3: Run Inference & Ensemble

**1. Inference:** Generate predictions for both experts.
```bash
python Expert1/inference.py
python Expert2/inference.py
```
**2. Ensemble:** Merge the inference results from Expert 1 and Expert 2 in logit space.
```bash
python ensemble.py
```
**3. Format Check & Video Generation:** Validate your submission format and render the predicted saliency outputs onto the source video frames.
```bash
python check.py
python makevideos.py
```

### Step 4: Training (Optional)
If you wish to train the model from scratch, run the two-stage LoRA fine-tuning pipeline:
```bash
python trainnew.py   # Stage 1
python trainnew2.py  # Stage 2
```

---

## ⚠️ Limitations & Notes

**Disclaimer:** This framework and its pre-trained weights are intended for **academic research purposes only**.
- The model relies heavily on the InternVideo2 backbone; out-of-memory (OOM) errors may occur on GPUs with less than 24GB VRAM.
- Inference speed and performance may fluctuate depending on the hardware utilized.

---

## 🤝 Acknowledgements & Contact

- **Contact:** If you have any questions or encounter issues, feel free to open an issue or contact the author Kun Wang at `khylon.kun.wang@gmail.com`.

---

## 📝⭐️ Citation

If you find this project useful for your research, please consider citing:


@inproceedings{ntire26visage, 
  title={{ViSAGE @ NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results}}, 
  author={Wang, Kun and Hu, Yupeng and Li, Zhiran and Liu, Hao and Xiang, Qianlong and Nie, Liqiang},  
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},  
  year={2026} 
}