5.2 Hierarchical Discriminative Learning 同时引入K个分层的多重分类任务: 特别地,我们将第i个实例的K个分层图像哈希表示输入给K个多层感知器,如下所示: 最后,采用负对数似然损失对K个分层判别分类: 式中ρk为第k层的置信度,log(·)为元素对数函数。 5.3 Regularized Cross-modal Hashing 在上面,我们已经考虑了...
句子的表示是最后一层transformer的平均值。 图2a显示了我们基于transformer的句子编码器的示意图,TR(.,\Theta),其中\Theta是模型参数,每个食谱组件的参数都不同。因此,我们提取标题嵌入如下: 图2:(a) Transformer Encoder, TR:给定一个配方句子,我们的模型使用Transformer编码器将其编码为固定长度的表示。(b)分层Tr...
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding. CVPR 2022[paper][code] Learnable PINs: Cross-Modal Embeddings for Person Identity. ECCV 2018[paper] Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. ...
Jifeng Dai, Kaiming He, Jian Sun.CVPR 2015 SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation.[pdf] Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen.ECCV 2015 2013 Semi-supervised Learning for Large Scale Image Cosegmentation.[pdf] ...
(Wang et al.2009), supervised Hierarchical Dirichlet Processes Zhang et al. (2013), Storkey and Dai (2014), and maximum margin supervised topic model,MedLDA(Zhu et al.2012a). These models have shown to improve document classification performance (Zhu et al.2013a; Jiang et al.2012; Zhu ...
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation.[pdf] Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen.ECCV 2015 Semi-supervised Learning for Large Scale Image Cosegmentation. ...
(DASFAA'2023) ML-KGCL: Multi-level Knowledge Graph Contrastive Learning for Recommendation [paper] (ICME'2023) Hierarchical and Contrastive Representation Learning for Knowledge-Aware Recommendation [paper] (WSDM'2023) Knowledge-Adaptive Contrastive Learning for Recommendation [paper]Generative...
How many semi-supervised learning methods are there? Many. Some often-used methods include: EM with generative mixture models, self-training, consistency regularization, co-training, transductive support vector machines, and graph-based methods. And with the advent of deep learning, the majority of...
cross-modal understanding, and predicting the most suitable answer. The two major factors that have the potential to enhance the effectiveness of VQA are the model architecture and the data. VQA is a data-intensive task that requires substantial amounts of labelled training data. A good dataset ...
On the other hand, deep-learning approaches utilize hierarchical feature extraction to adaptively capture depth and essential features, thereby enabling the representation of complex data structures through nonlinear transformations of the original input data. This enhances the model’s capacity for effective...