Multimodal Disentangled Representation Learning

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6 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion
Sep 30, 2026Zeyu Wang, Jiayu Wang, Haiyu Song +1Multi-Source Image Fusion Multimodal Disentangled Representation Learning
College of Computer Science and Engineering, Dalian Minzu University, Dalian, China · School of Artificial Intelligence and Robotics, Hunan University, Hunan, China · Department of Automation, Tsinghua University, Beijing, China
S2T-Unet: A Structure-to-Style Framework for Inter-Modality MRI Translation
Sep 29, 2026Yichao LiuImage-to-Image Translation Multimodal Disentangled Representation Learning
Heidelberg University, IWR, Germany
Structured Latent Modeling for Supervised Multimodal Information Decomposition
Sep 28, 2026Wanting Huang, Sanvesh Srivastava, Weiran WangMultimodal Disentangled Representation Learning Multimodal Learning
Department of Computer Science University of Iowa · Department of Statistics University of Iowa
Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings
Sep 28, 2026Duanning Chen, Ke He, Bin Yang +1Missing-Modality Learning Multimodal Disentangled Representation Learning
Wuhan University
M $^2$ PFN: End-to-End Disentangled Alignment for Generalizable Multimodal In-Context Learning in Alzheimer's Disease
Sep 23, 2026Lujia Zhong, Shuo Huang, Jianwei Zhang +2Multimodal Disentangled Representation Learning Multimodal ICL
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA · Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA · Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
Sep 21, 2026Xiaoqiang Lu, Licheng Jiao, Long Sun +5Multimodal Disentangled Representation Learning Referring Expression Segmentation
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence, Xidian University Xi’an, Shaanxi 710071, China
CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space
Aug 31, 2026Paul Schneider, Nazim HaouchineCross-Modal Representation Learning Multimodal Disentangled Representation Learning
Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA · Technical University of Munich, Munich, Germany
Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective
Aug 31, 2026Jiaqi Zhang, Zheng Pang, Mengting Li +9Missing-Modality Learning Memory-Augmented Neural Networks
Jiangsu University · Griffith University · National University of Singapore +8
Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval
Aug 12, 2026Najmeh Forouzandehmehr, Topojoy Biswas, Evren Korpeoglu +1Multimodal IR Multimodal Disentangled Representation Learning
Walmart Global Tech Sunnyvale, California, USA
FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data
Aug 11, 2026Viktoria Schuster, Sana Tonekaboni, Caroline UhlerIntrinsic Dimensionality Representation Learning
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA · Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA · Technical University of Denmark, Lyngby, Denmark +1
Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations
Aug 7, 2026Ioannis Ziogas, Ensieh Khazaei, Bilal Taha +4Variational Autoencoders Human Activity Recognition
Department of Biomedical Engineering and Biotechnology, Khalifa University, 127788 Abu Dhabi, UAE · Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada · MIT Media Lab +2
MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis
Aug 6, 2026Yuhua Wen, Yingying Zhou, Qifei Li +4Missing-Modality Learning Multimodal Disentangled Representation Learning
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Zhongguancun Academy, Beijing 100094, China · Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China +1
MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
Jul 18, 2026Sana Tonekaboni, Viktoria Schuster, Caroline UhlerCross-Modal Representation Learning Multimodal Disentangled Representation Learning
Massachusetts Institute of Technology, Cambridge, MA, USA · Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA · Vector Institute, Toronto, Canada +1
RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
Jun 3, 2026Vasiliki Rizou, Pascal Frossard, Dorina ThanouDisentangled Representation Learning Cross-Modal Representation Learning
EPFL
Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding
Jun 2, 2026Zhengyi Zhao, Shubo Zhang, Zezhong Wang +6Multimodal Disentangled Representation Learning Representation Disentanglement
The Chinese University of Hong Kong · Huawei · Central China Normal University +3
DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement
May 25, 2026Renjie Lu, Xulong Zhang, Xiaoyang Qu +2Multimodal Disentangled Representation Learning Unified Multimodal Models
Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China · University of Science and Technology of China, Hefei, China.
Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models
May 21, 2026Piotr Kubaty, Patryk Marszałek, Łukasz Struski +3Disentangled Representation Learning Multimodal Disentangled Representation Learning
Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook
May 18, 2026Zeyu Chen, Jie Li, Kai HanCross-Modal Learning Multimodal Disentangled Representation Learning
Visual AI Lab, The University of Hong Kong
Controlla: Learning Controllability via Graph-Constrained Latent Geometry
May 15, 2026Jamuna S. Murthy, Amin Karimi Monsefi, Rajiv RamnathDisentangled Representation Learning Multimodal Disentangled Representation Learning
Ramaiah Institute of Technology · The Ohio State University
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
Apr 20, 2026Sijie Mai, Shiqin HanDisentangled Representation Learning Causal Representation Learning
School of Computer Science, South China Normal University
SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis
Feb 26, 2026Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3Disentangled Representation Learning Multimodal Disentangled Representation Learning
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA · Department of Radiology, University of Washington, Seattle, WA, USA

Sep 30, 2026, cs.CVJ/K move · Enter open · S save

When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion

Zeyu Wang, Jiayu Wang, Haiyu Song, Haoran Duan

College of Computer Science and Engineering, Dalian Minzu University, Dalian, China · School of Artificial Intelligence and Robotics, Hunan University, Hunan, China · Department of Automation, Tsinghua University, Beijing, China

Multi-Source Image Fusion Multimodal Disentangled Representation Learning Self-Supervised Learning Multimodal Fusion

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Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a high-quality fused image and support downstream tasks. Recently, feature decomposition has become an important paradigm by separating source images into common and modality-specific unique features. However, existing methods lack clear supervision because ground-truth (GT) decomposition feature maps are unavailable. They usually combine multiple image-level metrics as losses, which are inherently incomplete and may conflict since each pixel couples attributes such as texture, edge, and contour. To address this, we propose a 1D signal-level self-supervised feature decomposition paradigm. Our core insight is to reformulate feature decomposition from unclear 2D image-level supervision into an integral-driven 1D signal-level optimization problem. This objective-level reformulation uses the 1D signal form to compute the integral constraint. The decomposer is optimized by the integral area between common and original signals, enabling more stable optimization with a clear optimization objective. Our model follows a two-stage SSL framework. Stage I designs dual pretext tasks for integral-driven decomposition at the signal level and structure-preserving reconstruction at the image level. Stage II fuses unique features and combines them with common features to reconstruct the fused image. Experiments on representative MMIF tasks show state-of-the-art (SOTA) performance. Code: github.com/Wangjiayu0512/SIDFusion.

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