2、freeze the pre-trained model and only tune the input data (e.g., prompt) for model adaptation(冻结网络只调整少量参数) Motivation for work:learn transferable (domain-agnostic) prompts to effectively leverage both pre-trained knowledge and source-knowledge for the target domain using CLIP. 文...
无监督域适应 (UDA) 将预测模型有标签的源域转移到无标签的目标域 → 而在有些场景下,源域的标签收集也很昂贵,之前的工作在有监督的源域场景下提出的,这些工作没有考虑到源域标签收集困难的问题 → 为了解决这个问题,最近的工作使用”实例级别的跨域自监督学习+微调“的方法 → 而基于实例的自监督学习存在几个...
A more practical setting is to utilize a large-scale pre-trained model to fill the domain gap. For example, CLIP shows promising zero-shot generalizability to bridge the gap. However, after applying traditional fine-tuning to specifically adjust CLIP on a target domain, CLIP suffers from ...
The text embeddings are generated through our LLM Domain Template process, where an LLM is used to generate source and target domain descriptions that are fed to a frozen CLIP model and combined. In experiments on four benchmarks we show that a model trained using CoPT achieves the new state...
4.2.4 Adaptation between dissimilar domains 在现实世界图像向艺术图像的转换过程中,现实世界图像的内在特征、质感在艺术图像中完全不同。 因此,很多方法考虑了不同领域的适应性,有三个适应性设置:PASCAL-VOC对Clipart, PASCAL-VOC对Watercolor和PASCAL-VOC对Comic。 所有这些领域的变化都是从真实世界的图像到合成的艺...
Similarly, CLIP2video [18] focuses on the spatial semantics captured by the CLIP model. Different from them, we explore the video-text retrieval task through the lens of unsupervised domain adaptation. Unsupervised Domain Adaptation. UDA transfers predictive models fr...
Relative to MaskCLIP (which is comparable), our model shows improvements of 12.2 and 12.5 mIoU on COCO and CoCA, respectively. In Tab. 5, we evaluate our method in the unsupervised domain adaptation setting, where the model is trained with images retrieved from the ImageNet1...
and we used the mean rating for each clip for each of the 14 affective dimensions as input to our analyses. A separate subset of participants made categorical (yes/no) ratings of 34 emotion categories. We used mean response data, where a value of 1 indicated that the given emotion category...
Domain adaptation is a type of transfer learning utilized to train a model with unseen data in the target domain by acquiring knowledge from a related source domain [2]. The source domain refers to the data distribution used to train the model with labeled data for the source task, while th...
基础分支利用提示调整来增强CLIP模型的可辨别性。对于对齐分支,我们设计了一种图像引导的特征调整来减少领域差异。 Preliminaries 1、Unsupervised Domain Adaptation 无监督领域自适应(UDA)专注于利用来自源领域标记数据和目标领域未标记数据来提升模型的泛化性能。具体来说,给定一个源领域的标记数据集 D_{s} = \{x_...