📹 (ACM MM 2025) HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval (Model Weights)
1School of Software, Shandong University
2School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen),
✉ Corresponding author
This repository hosts the official pre-trained model weights for **HUD**, a novel framework tackling both Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR) tasks by explicitly leveraging the disparity in information density between modalities.
---
## 📌 Model Information
### 1. Model Name
**HUD** (Hierarchical Uncertainty-Aware Disambiguation Network) Checkpoints.
### 2. Task Type & Applicable Tasks
- **Task Type:** Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR).
- **Applicable Tasks:** Retrieving a target video or image based on a reference visual input and a text modifier. HUD excels at addressing modification subject referring ambiguity and limited detailed semantic focus.
### 3. Project Introduction
**HUD** is the first framework that explicitly leverages the disparity in information density between video and text. It achieves State-of-the-Art (SOTA) performance through three key modules:
- 🎯 **Holistic Pronoun Disambiguation:** Exploits overlapping semantics through holistic cross-modal interaction to indirectly disambiguate pronoun referents.
- 🔍 **Atomistic Uncertainty Modeling:** Discerns key detail semantics via uncertainty modeling at the atomistic level, enhancing focus on fine-grained visual details.
- ⚖️ **Holistic-to-Atomistic Alignment:** Adaptively aligns the composed query representation with the target media by incorporating a learnable similarity bias.
### 4. Training Data Source & Hosted Weights
The HUD framework supports both video and image retrieval benchmarks. This repository provides pre-trained checkpoints evaluated on the following datasets:
* **CVR:** WebVid-CoVR dataset.
* **CIR:** FashionIQ and CIRR datasets.
*(Note: Download the respective `.ckpt` files hosted in the "Files and versions" tab of this repository).*
---
## 🚀 Usage & Basic Inference
These weights are designed to be evaluated using the highly modular, Hydra-configured [HUD GitHub repository](https://github.com/ZivChen-Ty/HUD).
### Step 1: Prepare the Environment
We recommend using Anaconda. Clone the repository and install dependencies:
```bash
git clone https://github.com/iLearn-Lab/MM25-HUD
cd MM25-HUD
conda create -n hud python=3.8.10 -y
conda activate hud
conda install pytorch==2.1.0 torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt
```
### Step 2: Download Model Weights
Download the specific checkpoints from this Hugging Face repository and place them into your local directory. Ensure your dataset paths are correctly configured in `configs/machine/default.yaml`.
### Step 3: Run Evaluation
To evaluate a trained model, use `test.py` and specify the target benchmark and checkpoint path via Hydra overrides:
```bash
python3 test.py \
model.ckpt_path=/path/to/your/downloaded_checkpoint.ckpt \
+test=webvid-covr # or fashioniq / cirr-all
```
---
## ⚠️ Limitations & Notes
- **Configuration:** HUD is entirely managed by **Hydra** and **Lightning Fabric**. Make sure to override configurations via the CLI or modify the YAML files in the `configs/` directory as needed.
- **Hardware & Environment:** The project was specifically developed and tested on Python 3.8.10, PyTorch 2.1.0, and an NVIDIA A40 48G GPU. Using significantly different environment settings may impact reproducibility.
---
## 📝⭐️ Citation
If you find our framework, code, or these weights useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repository and citing our ACM MM 2025 paper:
```bibtex
@inproceedings{HUD,
title = {HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval},
author = {Chen, Zhiwei and Hu, Yupeng and Li, Zixu and Fu, Zhiheng and Wen, Haokun and Guan, Weili},
booktitle = {Proceedings of the ACM International Conference on Multimedia},
pages = {6143–6152},
year = {2025}
}
```