In this paper, we address the above problems and propose an efficient lightweight image steganalysis method (ELMANet) based on cascaded multiscale neural networks and the multiscale hybrid attention mechanism to extract more significant features from steganographic images. The architecture of ELMANet ...
In this section, we first formulate the multi-scale feature fusion problem, and then introduce the two main ideas for our proposed BiFPN: efficient bidirectional cross-scale connections and weighted feature fusion. Figure 2: Feature network design – (a) FPN [16] introduces a top-down pathway ...
It is critical to model the complex interactions in the protein pocket–ligand complexes for pocket generation. However, the multi-granularity (for example, atom level and residue level) and multi-aspect (intraprotein and protein–ligand) nature of interactions brings a lot of challenges. Inspired ...
Current methods for removing moire patterns mostly extract multiscale information by downsampling pooling layers, which may inevitably cause information loss. To address this issue, this paper proposes a demoireing method in the wavelet domain. By employing both discrete wavelet transform (DWT) and ...
In this section, we first formulate the multi-scale feature fusion problem, and then introduce the two main ideas for our proposed BiFPN: efficient bidirectional cross-scale connections and weighted feature fusion. Figure 2: Feature network design – (a) FPN [16] introduces a top-down pathway ...
A multiscale super-patch local aggregation (MSSPLA) module is introduced to extract and aggregate the multiscale features and context information of scene super-patches. A super-patch transformer (SPT) module based on self-attention is given for effectively learning the feature similarities between ...
Then, the resulting feature maps are fed into the efficient additive attention block, which aims to learn con- textual information at each scale of the input size. Finally, the output feature maps are fed into a Linear block, which compose...
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation IEEE Access, 8 (2020), pp. 179656-179665, 10.1109/ACCESS.2020.3025372 View in ScopusGoogle Scholar [32] D. Bolya, C. Zhou, F. Xiao, Y.J. Lee, YOLACT++: Better Real-time Instance Segmentation, IEEE Trans. Pattern...
As mentioned above, multiscale feature fusion is utilized in most algorithms, which leads to a significant increase in network parameters and computation. However, offsets are relatively large and generally exceed 1 pixel. If only the tanh function is added as an activation function, the offset ...
TRN-Multiscale [58] TRN-Multiscale (our impl.) Two-stream TRNRGB+Flow [58] ECO [61] ECO [61] ECOEnLite [61] ECOEnLiteRGB+Flow [61] I3D from [50] Non-local I3D from [50] Non-local I3D + GCN [50] Backbone BNInception ResNet-50 BNInception ResNet-50...