The field of multimodal learning and representation is rapidly advancing, with a focus on developing innovative methods for integrating and analyzing multiple forms of data. Recent research has explored the use of diffusion-based models, hybrid architectures, and attention mechanisms to improve the accuracy and robustness of multimodal systems. Notably, the integration of multimodal data, such as images, text, and sensor readings, has shown great promise in applications like medical imaging, emotion recognition, and object detection. Furthermore, researchers are investigating new approaches to address challenges like modality collapse, missing data, and extreme modality imbalance. Overall, the field is moving towards more sophisticated and flexible models that can effectively capture complex relationships between different modalities and improve performance in a wide range of tasks. Noteworthy papers include Diff3M, which proposes a novel multi-modal diffusion-based framework for anomaly detection in medical imaging, and RoHyDR, which introduces a robust hybrid diffusion recovery method for incomplete multimodal emotion recognition.
Advances in Multimodal Learning and Representation
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Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays
RoHyDR: Robust Hybrid Diffusion Recovery for Incomplete Multimodal Emotion Recognition
Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification
Latent Mode Decomposition
ICPL-ReID: Identity-Conditional Prompt Learning for Multi-Spectral Object Re-Identification
Multi-task Learning For Joint Action and Gesture Recognition
Learning Shared Representations from Unpaired Data
A Novel Convolutional Neural Network-Based Framework for Complex Multiclass Brassica Seed Classification
Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment
Concentration Distribution Learning from Label Distributions
Learning A Robust RGB-Thermal Detector for Extreme Modality Imbalance
Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer
A Closer Look at Multimodal Representation Collapse
A hybrid PDE-ABM model for angiogenesis and tumour microenvironment with application to resistance in cancer treatment
Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision
Hierarchical Material Recognition from Local Appearance
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
MCFNet: A Multimodal Collaborative Fusion Network for Fine-Grained Semantic Classification
PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening
Color Image Set Recognition Based on Quaternionic Grassmannians
ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer