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README.md

@@ -1,3 +1,104 @@
 ---
 license: apache-2.0
+language:
+- en
+library_name: pytorch
+tags:
+- dynamic-facial-expression-recognition
+- dfer
+- audio-visual
+- emotion-recognition
+- affective-computing
+- multimodal
+- mamba
+- state-space-model
+- www2026
 ---
+
+<div align="center">
+
+# BHGap · Trained Checkpoints
+
+### A Deep Iterative Prompting and Multi-stage Alignment Framework for Dynamic Facial Expression Recognition
+
+<p>
+  <a href="https://doi.org/10.1145/3774904.3792417"><img src="https://img.shields.io/badge/Venue-WWW%202026%20(Oral)-1f6feb?style=flat-square" alt="WWW 2026 Oral"></a>
+  <a href="https://doi.org/10.1145/3774904.3792417"><img src="https://img.shields.io/badge/Paper-ACM%20DL-b31b1b?style=flat-square" alt="Paper"></a>
+  <a href="https://github.com/NDYZD666/-public-BHGap"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=flat-square&logo=github&logoColor=white" alt="GitHub"></a>
+  <img src="https://img.shields.io/badge/Task-Audio--Visual%20DFER-8957e5?style=flat-square" alt="Task">
+</p>
+
+</div>
+
+> 🤗 This repository hosts the **trained model weights** for **BHGap** (WWW 2026 Oral).
+> For the full code, training and evaluation pipeline, please visit the **[GitHub repository](https://github.com/NDYZD666/-public-BHGap)**.
+
+<div align="center">
+<img src="https://huggingface.co/NiDeYingZiD/BHGap-ckpt/resolve/main/architecture.png" width="92%" alt="BHGap architecture">
+<br>
+<em>Frozen audio-visual encoders + SDIC (iterative cross-modal prompting) + MSC2F (coarse-to-fine alignment) + lightweight fusion.</em>
+</div>
+
+## 📖 Abstract
+
+Dynamic Facial Expression Recognition (DFER) is a crucial part of affective computing, with broad applications in human-computer interaction and social media content analysis. Effectively integrating audio-visual signals remains the core challenge, as existing approaches are constrained by **(1) shallow, static fusion** that fails to capture the dynamic co-evolution of features, and **(2) implicit, coarse alignment** that cannot bridge the modality gap.
+
+**BHGap** reformulates audio-visual collaboration from a one-shot fusion event into a continuous, reciprocal generation process spanning every layer of frozen backbone encoders:
+
+- **SDIC** — an SSM (Mamba)-based **Cross-Modal Prompt Generator** dynamically produces *guidance prompts* for the counterpart modality at each encoding layer, enabling deep and fine-grained feature co-evolution.
+- **MSC2F** — a coarse-to-fine alignment module that combines **low-rank adversarial alignment** (macro-level distribution & spatio-temporal congruity) with **MMD-driven implicit differentiation** (micro-level statistical & semantic consistency).
+
+It achieves **state-of-the-art** performance on **DFEW** and **MAFW** using raw audio-visual inputs only.
+
+## 📊 Results
+
+Evaluated under 5-fold cross-validation (**WAR** = Weighted Average Recall, **UAR** = Unweighted Average Recall).
+
+| Dataset | Modality | WAR (%) | UAR (%) |
+| --- | :---: | :---: | :---: |
+| **DFEW** | Audio + Visual | **78.80** | **69.03** |
+| **MAFW** | Audio + Visual | **59.97** | **47.68** |
+
+## 📦 Checkpoints
+
+This repository provides the trained BHGap checkpoints for **DFEW** and **MAFW** (5-fold). Each fold produces the best models selected by WAR / UAR (`model_best_war.pth`, `model_best_uar.pth`); see the **Files and versions** tab for the exact contents.
+
+## 🚀 Usage
+
+Download the checkpoints from the Hub:
+
+```bash
+huggingface-cli download NiDeYingZiD/BHGap-ckpt --local-dir ./checkpoints
+```
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(repo_id="NiDeYingZiD/BHGap-ckpt", local_dir="./checkpoints")
+```
+
+Then evaluate with the code from the [GitHub repository](https://github.com/NDYZD666/-public-BHGap):
+
+```bash
+python evaluate.py --dataset DFEW --checkpoint ./checkpoints/model_best_war.pth --fold 5
+```
+
+> **Note** — These are task checkpoints for the BHGap pipeline (frozen MAE-Face / AudioMAE backbones + trainable SDIC & MSC2F modules), not a standalone `transformers` model. Please load them through the BHGap code.
+
+## 📌 Citation
+
+```bibtex
+@inproceedings{zhang2026bhgap,
+  title     = {BHGap: A Deep Iterative Prompting and Multi-stage Alignment Framework for Dynamic Facial Expression Recognition},
+  author    = {Zhang, Yichi and Han, Yunqi and Ding, Jiayue and Chen, Liangyu},
+  booktitle = {Proceedings of the ACM Web Conference 2026 (WWW '26)},
+  year      = {2026},
+  address   = {Dubai, United Arab Emirates},
+  publisher = {Association for Computing Machinery},
+  doi       = {10.1145/3774904.3792417}
+}
+```
+
+## 🙏 Acknowledgements
+
+Built upon [MAE-Face](https://github.com/FuxiVirtualHuman/MAE-Face), [AudioMAE](https://github.com/facebookresearch/AudioMAE), [Mamba](https://github.com/state-spaces/mamba), [MMA-DFER](https://github.com/katerynaCh/MMA-DFER), and the [DFEW](https://dfew-dataset.github.io/) / [MAFW](https://mafw-database.github.io/MAFW/) datasets. Thank you for these excellent efforts!