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Found 7 papers, 4 papers with code
Make-it-Real: Unleashing Large Multimodal Model's Ability for Painting 3D Objects with Realistic Materials
Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions.
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting.
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms.
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
We conducted a series of structured experiments to evaluate their performance in various industrial application scenarios, offering a comprehensive perspective on their practical utility.
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents.
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting progress in 3D language understanding and generation.
Improving Covariance-Regularized Discriminant Analysis for EHR-based Predictive Analytics of Diseases
In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier.
