Update dataset card with CalMRL info and task category

This PR improves the dataset card by linking it to the paper [Calibrated Multimodal Representation Learning with Missing Modalities](https://huggingface.co/papers/2511.12034) and the [official GitHub repository](https://github.com/Xiaohao-Liu/CalMRL). It also adds the `feature-extraction` task category to improve discoverability.

README.md

@@ -1,18 +1,15 @@
 ---
 license: mit
+task_categories:
+- feature-extraction
 ---
 
-
-
 <div align="center">
 
-<h1><a color="red" href="https://arxiv.org/pdf/2507.17343">Principled Multimodal Representation Learning (PMRL)</a></h1>
+<h1><a color="red" href="https://huggingface.co/papers/2511.12034">Calibrated Multimodal Representation Learning with Missing Modalities (CalMRL)</a></h1>
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-![License](https://img.shields.io/badge/Accepted-TPAMI'2026-purple)
-[![License: MIT](https://img.shields.io/badge/Github-PMRL-black.svg)](https://github.com/Xiaohao-Liu/PMRL)
-
-*A Novel Framework for Representation Learning Across Multiple Modalities*
+[![Github](https://img.shields.io/badge/Github-CalMRL-black.svg)](https://github.com/Xiaohao-Liu/CalMRL)
 
 </div>
 
@@ -20,60 +17,39 @@ license: mit
 
 ## ✨ Overview
 
-![](img/top.png)
-
-**Principled Multimodal Representation Learning (PMRL)** addresses the fundamental challenges in multimodal representation learning by proposing a novel framework that achieves simultaneous alignment of multiple modalities without anchor dependency.
-
-### 💡 Our Solution
+This repository contains data and code for **CalMRL**, a framework designed to calibrate incomplete alignments caused by missing modalities in multimodal representation learning. 
 
-PMRL introduces a principled approach grounded in **theoretical insights**:
+Multimodal representation learning harmonizes distinct modalities by aligning them into a unified latent space. CalMRL leverages priors and inherent connections among modalities to model the imputation for missing ones at the representation level, addressing the "anchor shift" problem where observed modalities align with local anchors that deviate from the optimal ones.
 
-> **Key Insight**: Full alignment corresponds to a rank-1 Gram matrix
-
-Our framework optimizes the dominant singular value of the representation matrix to align modalities along a shared leading direction.
+For more details, please refer to the paper: [Calibrated Multimodal Representation Learning with Missing Modalities](https://huggingface.co/papers/2511.12034).
 
 ---
 
 ## 🎯 Key Features
 
-🔄 **Simultaneous Multi-Modal Alignment**
-- No predefined anchor modality required
-- Unified representation space for all modalities
-
-🧮 **Softmax-based Loss Function**
-- Treats singular values as logits
-- Prioritizes the largest singular value for stable optimization
-
-🎯 **Instance-wise Contrastive Regularization**
-- Maintains inter-instance separability
-- Prevents representation collapse
-
-⚡ **Distributed Training Support**
-- Multi-GPU training capabilities
-- Efficient data parallel processing
-
-📊 **Comprehensive Evaluation**
-- Extensive benchmarking across diverse tasks
-- Quantitative and qualitative analysis tools
+- **Calibration of Incomplete Alignments**: Models imputation for missing modalities at the representation level.
+- **Anchor Shift Mitigation**: Provides theoretical insights into how missing modalities cause local anchors to deviate from optimal ones.
+- **Bi-step Learning**: Employs a bi-step learning method with a closed-form solution for the posterior distribution of shared latents.
+- **Flexibility**: Offers the ability to absorb data with missing modalities by equipping calibrated alignment with existing advanced methods.
 
 ---
 
-## 🏗️ Architecture
-
-![](img/framework.png)
-
-The PMRL framework consists of three main components:
-
-1. **🔧 Multi-Modal Encoder**: Processes different input modalities
-2. **🎯 Singular Value Optimization**: Aligns representations via dominant singular value
-3. **🔄 Principled Regularization**: Maintains instance-level discrimination
+## 🏗️ Related Work: PMRL
 
+This work builds upon or relates to [Principled Multimodal Representation Learning (PMRL)](https://github.com/Xiaohao-Liu/PMRL). PMRL addresses fundamental challenges in multimodal representation learning by proposing a framework that achieves simultaneous alignment of multiple modalities without anchor dependency.
 
 ## Citation
 
 If you find this work useful, please consider citing:
 
 ```bibtex
+@article{liu2025calibrated,
+  title={Calibrated Multimodal Representation Learning with Missing Modalities},
+  author={Liu, Xiaohao and Xia, Xiaobo and Wei, Jiaheng and Yang, Shuo and Su, Xiu and Ng, See-Kiong and Chua, Tat-Seng},
+  journal={arXiv preprint arXiv:2511.12034},
+  year={2025}
+}
+
 @article{liu2026principled,
   title={Principled multimodal representation learning},
   author={Liu, Xiaohao and Xia, Xiaobo and Ng, See-Kiong and Chua, Tat-Seng},
@@ -83,12 +59,8 @@ If you find this work useful, please consider citing:
 }
 ```
 
-
-
 <div align="center">
 
+**[🔝 Back to Top](#calibrated-multimodal-representation-learning-with-missing-modalities-calmrl)**
 
-**[🔝 Back to Top](#-principled-multimodal-representation-learning-pmrl)**
-
-</div>
-
+</div>