For the first issue, we first conduct cross-modality image translation, then enable the encoder to extract modality-invariant feature representation by aligning translated versions with original samples, and finally use cross-modality clustering and hard sample contrastive loss to handle the hard samples...
Meanwhile, for the individual part, a cross-modality triplet (CMT) loss was employed to distinguish the pedestrian images with different identities. Adversarial loss: Some works [60,63,68,81] also use adversarial learning to learn modality-invariant features. For example, [60,63] designed a ...
每一个模态 都提取出序列特征, 我们把这个seq 通过一个LSTM, 并且LSTM的 最后一个隐层接一个全连接映射到同一维度 2.1.2 Modality-Invariant and -Specific Representations 我们把同一个特征 映射到两个不同的特征空间中, 一个是模态不变特征, 一个是模态特有特征。 作者认为 模态不变 和 模态特有特征 提供了...
The syncretic modality [27] is proposed to guide the generation of discriminative and modality-invariant representations. The DFM [7] acquires the mixed modality by integrating visible and infrared pixels. However, these methods generate the auxiliary modality by directly f...
1) which are sparse, scale- and rotation invariant, hence they are expected to perform well even with rigidly transformed or cropped queries. The BoW is defined on the features extracted from the CoMIRs of all the images in the searchable repository, by K-means clustering using a suitable ...
文章目录1.前言2.模型结构2.1ModalityRepresentation Learning2.1.1 Utterance-level Representations2.1.2Modality-Invariant and -Specific Representations2.2ModalityFusion2.3 Learning2.3.1 Similarity Loss2.3.2 Difference Loss2.3.3 Reconstruction Loss2.3.4 Task Loss3. ...
2. The guiding effect of self-supervision in the training process is rather beneficial when learning modality-invariant feature representations. Conclusion: In this article, we propose S2-Net, which introduces the self-supervised learning in the training learn the modality-invariant feature ...
Based on the final clusters, a modalityinvariant marginalized kernel is then computed, where the similarities between the reconstructed features of each modality are aggregated across all clusters. Our framework enables the reliable inference of semantic-class category for an image, even across large ...
Therefore, it would be better to utilize temporal signals when learning modality-invariant representations to address this challenge. To address the two challenges, we propose a modality-invariant temporal representations learning strategy and design a new gated inter-modality attention mechanism. For the...
Hu et al. (2021) proposed an Adversarial Disentanglement and Correlation Network (ADCNet) toward learning modality-invariant and discriminative representations of pedestrians. Show abstract Deep learning for visible-infrared cross-modality person re-identification: A comprehensive review 2023, Information ...