Contrastive learningCross attentionMeta-learningFew-shot object detection aims to learn to detect novel objects from only a few annotated samples. Most training frameworks adopt the fusing of high-dimensional features with semantic information on the support images to learn the recognition and ...
Motivation: 显存的attention模型中,对于attention没有直接的监督信号,所以本文提出了两个模块,在attention的过程中加入了监督信号,如下图所示: 1)CCR使得模型能够关注到相关的区域(尽可能多地关注到相关…
and ours of ETTh2 dataset. Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves bett...
Recent contrastive learning-based recommendation algorithms ignore the effect of sequences in single domain and cross domain, whereas the proposed infomax objective pays close attention to the influence of sequential interactions on user preferences in different domains, which improves the accuracy of the...
文章提出了一种新的CLIP(Contrastive Language-Image Pre-training)引导的对比学习方法,用于多模态特征对齐(CLFA,CLIP-guided Contrastive-Learning-based Feature Alignment)。这种方法旨在将不同模态(如图像和文本)提取的特征投影到统一的深度空间中,以实现跨模态的深度信息交互。 文章首先介绍了多模态语义理解的背景和...
The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this non-trivial problem relies on graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However...
[IJBHI 2023] This is the official implementation of CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation accepted to IEEE Journal of Biomedical and Health Informatics (J-BHI), 2023. cross-modal-learning medical-report-generation radiology-report-generation Updated Apr...
Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistiv... H Sun,Y Ding,J Bao,... - 《Neural Net...
Their approach yielded notable performance gains on both SLAKE and VQA-RAD datasets over the pipeline approaches. However, contrastive learning primarily focuses on capturing the global relationship between the entire medical image and the question, but it is less effective to capture precise ...
第一阶段预训练主要包括4个任务: self-supervised masked language modeling、两个lexicon-bottlenecked masked language modelings、in-batch lexicon-contrastive learning。第一阶段预训练的整体结构图如下。 Self-supervised masked language modeling:基础的MLM任务,mask掉一部分token后对这部分token进行预测,主要是训练文...