Propose a sentiment knowledge-enhanced attention fusion network (SKEAFN); Build an additional knowledge graph to leverage explicit sentiment information; Use a multi-head attention mechanism to model the interactions among modalities; Develop feature-wised attention to adjust the contributions of multiple modalities.

An efficient desnowing method named Snowed Autoencoder (SAE) is proposed; A multi-encoder for SAE using snow information with an attention module is proposed; A multi-decoder for SAE with fewer sub-decoders than sub-encoders is proposed; SAE outperformed state-of-the-art on four desnowing datasets.

Multi-scaled feature-fused image dehazing network called MFID-Net is proposed; MFID-Net uses dynamic dehazing convolution and activation to improve image dehazing; The proposed dynamic dehazing convolution utilizes dynamic weight fusion; The proposed dynamic dehazing activation uses input global context encoding function; MFID-Net achieves the state-of-the-art performance on dehazing datasets.

To alleviate the above issue, we propose a new architecture that combines cross-modal knowledge transfer from visual to audio modality into our semi-supervised learning method with consistency regularization. We posit that introducing visual emotional knowledge by the cross-modal transfer method can increase the diversity and accuracy of pseudo-labels and improve the robustness of the model. To combine knowledge from cross-modal transfer and semi-supervised learning, we design two fusion algorithms, i.e. weighted fusion and consistent & random.

Will be updated soon.