Image understandingZero-shot learningImage classificationKnowledge representationGenerative adversarial networkSemi-supervised learningNew categories can be discovered by transforming semantic features into synthesized visual features without corresponding training samples in zero-shot image classification. Although ...
Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen classes share a common semantic space. A number of methods propose to desig
Proponents of these image embedding systems have stressed their advantages over the traditionalway{} classification framing of image understanding, particularly in terms of the promise for zero-shot learning -- the ability to correctly annotate images of previously unseen object categories. In this ...
Mapping Images to Context-Dependent Words for Accurate Zero-Shot Composed Image Retrieval. GitHub - Pter61/context-i2w: Context-I2W: Mapping Images to Context-dependent words for Accurate Zero-Shot Composed Image Retrieval [AAAI 2024 Oral] 预训练阶段(左图):图像到上下文相关词映射的目标是从视角级...
Zero-shot learning：在模型学习的过程中，我们对某些类别（定义为Unseen Classes）的所有样本均不能使用...
Attribute-based zero-shot learning Attribute-based ZSL models are most often used for computer vision tasks. They work by training on human-labeled datasets of images. The labels consist of attributes the person labeling considers useful. For each image, the person applies a text description of ...
3. Zero-shot learning using coupled dictionary learning In the standard dictionary learning framework, a sparsifying dictionary is learned using a given training sample set X=[x1,…,xN] for a particular class of signals. Unlike standard dictionary learning, coupled dictionary learning has been propos...
Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV 2023) zero-shotnovel-view-synthesisimage-to-3dsingle-view-reconstructionstable-diffusion UpdatedDec 5, 2023 Python prs-eth/Marigold Star2.6k [CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators...
这篇文章主要探求的是对于zero-shot的对抗鲁棒性,其中训练损失和adaption方法是本文探求的对象。text-guided contrastive adversarial training loss提出然后应用于model finetuning以及visual prompt tuning。VPT在缺失文本指导的情况下效果更好,而finetuning在有指导的情况下效果更好。总的来说大大的提升了zero-shot的对抗...
Zero-shot image classification is a technique in computer vision where a model can classify images into categories that were not present during training. This is achieved by leveraging semantic information about the categories, such as textual descriptions or relationships between classes....