Specifically, our method performs processing only on the original feature maps without an extra assisting network. Moreover, we use cross-layer feature fusion to enhance the attention on shallow feature maps. By
During the bidirectional feature fusion process, the spectral information contained in each modality is learned. Additionally, we design Residual Multiplicative Connections (RMC) to update the fused features at each layer. At the decoding stage, we utilize a Feature Pyramid Aggregation Network (FPN) ...
Memf: multi-level-attention embedding and multi-layer-feature fusion model for person re-identification Pattern Recogn. (2021) L. Wan et al. G2da: geometry-guided dual-alignment learning for rgb-infrared person re-identification Pattern Recogn. (2023) C. Zhang et al. Crossing generative advers...
Each layer is concatenat- ed with all previous layers along the channel dimension, achieving feature reuse and serving as the input for the next layer. This alleviates the phenomenon of gradient vanishing and enables better performance with fewer parameters and computations. The design philosophy of...
Finally, the fusion vector is spliced and fused with the generated text for multimodal metaphor recognition. Our contributions are as follows: 1. We propose a fusion framework tailored for multimodal metaphor recognition, which involves the multi-layer fusion of features from different modalities. ...
Hence, the resulting feature descriptor is of the same dimension for both the full sized images in the searchable repository, as well as for the query image. The average pooling layer of the network blurs out the features which result from the common region of the full sized image and the...
In the prediction stage, the LiteCCLKNet is applied for classification. First, a lightweight 1 × 1 convolutional layer is fed with the input sample xi and generates a feature map . Note that C2 is slightly larger than C1, which aims to enhance the expressiveness of feature via mapping the...
Additionally, inserting a convolutional layer at the end of the CHTB helps to introduce more inductive bias to the transformer for better reconstruction. 3.3. Cross-scale hierarchical transformer block Given an input feature Fi,0 to the 1-st CHTB in the i-th transformer group, denoted as hi,...
The domain discriminator D obtains the domain probability distribution p(probability) of the sample through the fully connected layer and the softmax function, which is used for domain relationship modeling. In the domain discriminator, preal and pfake represent the training discrimination results of ...
each modality has one unified discriminator for both forward generators and coupled generators. The architecture of the discriminator is mainly composed of the Fully Connected layer (FC layer) and the activation layer. To minimize discrimination error, the loss function of the discriminator can be for...