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Found 10 papers, 3 papers with code
AMO Sampler: Enhancing Text Rendering with Overshooting
Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images.
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets.
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM).
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation.
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions.
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs.
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace.
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs).
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes.
