Search Results for author:
Found 2 papers, 1 papers with code
LLMGA: Multimodal Large Language Model based Generation Assistant
In the first stage, we train the MLLM to grasp the properties of image generation and editing, enabling it to generate detailed prompts.
Local and Global Logit Adjustments for Long-Tailed Learning
LGLA consists of two core components: a Class-aware Logit Adjustment (CLA) strategy and an Adaptive Angular Weighted (AAW) loss.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
