The field of multimodal learning is witnessing significant developments, with a focus on improving model performance and efficiency. Recent research has explored the use of collaborative multi-LoRA experts, achievement-based multi-task loss, and context-aware predictors to enhance multimodal information extraction and disease detection. Additionally, knowledge distillation techniques have been proposed to transfer knowledge from large models to smaller ones, enabling more efficient deployment in resource-constrained environments. Noteworthy papers in this area include Collaborative Multi-LoRA Experts with Achievement-based Multi-Tasks Loss for Unified Multimodal Information Extraction, which achieves superior performance on multiple benchmark datasets, and EmoVLM-KD, which fuses distilled expertise with vision-language models for visual emotion analysis, achieving state-of-the-art performance on multiple benchmark datasets.
Advancements in Multimodal Learning and Knowledge Distillation
Sources
Collaborative Multi-LoRA Experts with Achievement-based Multi-Tasks Loss for Unified Multimodal Information Extraction
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal
Emotion-Qwen: Training Hybrid Experts for Unified Emotion and General Vision-Language Understanding
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Efficient and Robust Multidimensional Attention in Remote Physiological Sensing through Target Signal Constrained Factorization
Seed1.5-VL Technical Report
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification
EmoVLM-KD: Fusing Distilled Expertise with Vision-Language Models for Visual Emotion Analysis
Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization
SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation
Neural Multivariate Regression: Qualitative Insights from the Unconstrained Feature Model
Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning