Contrastive self-supervised representation learning without negative samples for multimodal human action recognitiondoi:10.3389/fnins.2023.1225312HUMAN activity recognitionSUPERVISED learningHUMAN-computer interactionLEARNINGAction recognition is an important component of human-computer interaction, ...
论文:《INCREMENTAL FALSE NEGATIVE DETECTION FOR CONTRASTIVE LEARNING 》 来源:CVPR2021 ABSTRACT 最近,通过对比学习,自监督学习在计算机视觉任务中显示出巨大的潜力,其目的是区分数据集中的每个图像或实例。然而,这种实例级学习忽略了实例之间的语义关系,有时不希望从语义相似的样本中排除锚样本,这里称为“假负样本(...
How GCL Works without Positive Samples Positive Samples Are NOT a Must in GCL The Implicit Regularization of Graph Convolution in GCL How GCL Works without Negative Samples Graph Classification: Both Negative Samples and Specific Designs Are Not Needed Node Classification: Normalization in the Encoder...
To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving competitive performance, a recent work has attracted significant attention for providing a minimalist simple Siamese (SimS...
Unlike previous methods that use the rPPG signals as samples for contrastive learning, Sun et al. used the PSD corresponding to the rPPG signals as samples, with the PSD of the target video as the anchor and positive samples, while the PSD of another facial video as the negative samples. ...
Contrastive self-supervised representation learning without negative samples for multimodal human action recognition Action recognition is an important component of human-computer interaction, and multimodal feature representation and learning methods can be used to impro... H Yang,Z Ren,H Yuan,... - ...
2.2. Contrastive learning Contrastive learning, as the name implies, aims to learn data representations by contrasting positive and negative samples. It is at the core of recent works on self-supervised learning, generally using a contrastive loss called the InfoNCE loss. Contrastive Predictive Coding...
supervised contrastive learning have several advantages compared to conventional contrastive learning. First, the model can learn representations that are optimized specifically for the label transfer task, resulting in more distinguishable outcomes. Second, it reduced the sensitivity to negative samples. In...
Triplet Loss 通过比较一个锚点样本（anchor）与一个正样本（positive）和一个负样本（negative）之间的...
对比学习（Contrastive Learning）是近年来深度学习领域中的一个热点研究方向，尤其在自监督学习中显示出了...