Code:https://github.com/LLVM-AD/MAPLM One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models Paper:https://arxiv.org/pdf/2403.01849.pdf Code:https://github.com/TreeLLi/APT PromptKD: Unsupervised Prompt Distillation for Vision-Language Models Paper:https...
1、扩散模型改进 2、可控文生图 3、风格迁移 4、人像生成 5、图像超分 6、图像恢复 7、目标跟踪 8...
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection 作者:张小红(南京大学)、叶惠生(南京大学)、李静雯(南京大学)、汤沁宇(南京大学)、李元琪(南京大学)、过洁(南京大学)、郭延文(南京大学) 论文简介:我们提出了随机提示辅助弱监督3D对象...
One-Prompt to Segment All Medical Images. [Paper] [Code] 摘要:大型基础模型以其强大的零样本泛化能力而著称,在视觉和语言应用方面表现出色。然而,将它们应用于医学影像分割领域——一个包含多种成像类型和目标标签的领域,仍然是一个悬而未决的挑战。当前的方法,如调整交互式分割模型(如Segment Anything Model,简...
run: python val.py -net oneprompt -mod one_adpt -exp_name One-ISIC -weights *weight_path* -b 1 -dataset isic -data_path ../dataset/isic -vis 10 -baseline 'unet' change "data_path" and "exp_name" for your own useage. you can change "exp_name" to anything you want. You can...
Abstract Text-to-image (T2I) research has grown explosively in the past year owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet one pain point persists: the text prompt engineering and searching high-quality text prompts for cust...
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models Fri 21 Jun 5 p.m. RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception Fri 21 Jun 10:30 a.m. Delving into the Trajectory Long-tail Distribution for Muti-object Tracking ...
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models⭐code SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models⭐code RegionGPT: Towards Region Understanding Vision Language Model Enhancing Vision-Language Pre-training wi...
A Pedestrian is Worth One Prompt: Towards Language Guidance Person Re-Identification[Zexian Yang (...
68、VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation 关于文本到三维生成的创新推出了得分蒸馏抽样(Score Distillation Sampling,SDS),通过直接从二维扩散模型中提取先验知识,实现隐式三维模型(NeRF)的零样本学习。然而,当前基于SDS的模型在处理复杂的文本提示时仍存在困难,并且通常会导致3D模型出现畸变,...