Search Results for author:
Found 8 papers, 5 papers with code
Dense Connector for MLLMs
We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs.
Automated Multi-level Preference for MLLMs
To verify our viewpoint, we present the Automated Multi-level Preference (AMP) framework for MLLMs.
HARIS: Human-Like Attention for Reference Image Segmentation
In this paper, we propose a referring image segmentation method called HARIS, which introduces the Human-Like Attention mechanism and uses the parameter-efficient fine-tuning (PEFT) framework.
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations based on the learned representations.
GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?
Our study centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks: Firstly, we explore the potential of its generated rich textual descriptions across various categories to enhance recognition performance without any training.
RISAM: Referring Image Segmentation via Mutual-Aware Attention Features
Referring image segmentation (RIS) aims to segment a particular region based on a language expression prompt.
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection
In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle.
Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.
