Improving Fine-Grained Visual Understanding in Multi-Modal Models Through Multi-Scale Alignment
Multi-modal models can achieve a deeper, more accurate understanding of visual information by aligning object representations across multiple scales (text, coordinates, and images), leading to improved performance in tasks like grounding and object recognition.
Meta-Learned Modality-Weighted Knowledge Distillation (MetaKD): A Novel Approach for Robust Multi-Modal Learning with Missing Data
MetaKD, a novel meta-learning approach, effectively addresses the challenge of performance degradation in multi-modal learning when key modalities are missing by dynamically optimizing modality importance and performing modality-weighted knowledge distillation.
BALDUR: A Bayesian Approach for Combining Multi-Modal Data in Small Sample Classification
BALDUR is a novel Bayesian algorithm designed to improve classification accuracy in scenarios with limited sample sizes and high-dimensional, multi-modal biomedical data by combining data views into a common latent space and performing feature selection.
MMAR: Achieving Lossless Multi-Modal Auto-Regressive Probabilistic Modeling for Image Understanding and Generation
MMAR introduces a novel framework for joint image-text probabilistic modeling that overcomes information loss inherent in previous methods, achieving superior performance in both image understanding and generation by disentangling the diffusion process from the auto-regressive backbone and utilizing continuous image representations.
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding
TAMM verbessert die 3D-Formverständnis durch effektive Nutzung von Bild- und Textdaten.
© 2024 by Linnk AI