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...
Moreover, our advanced dual-constrained triplet loss is introduced for better cross-modality matching performance. The experiments on two cross-modality person re-identification datasets show that MANN can effectively learn modality-invariant features and outperform state-of-the-art methods by a large ...
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 ...
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 ...
In model training, the cross-modality feature alignment learning method is implemented, where the modality-invariant features are expected to be extracted. The domain adversarial learning strategy is applied on multiple layers, and the modality discriminator is a typical deep neural network module for ...
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. ...
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...
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance. To solve these problems, we propose a cross-modality ...
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 fea...
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 ...