Convolutional neural network (CNN)-based encoder-decoder models have profoundly
inspired recent works in the field of salient object detection (SOD). With the rapid …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …

Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …

Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …

Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …

End-to-end autonomous driving has made impressive progress in recent years. Existing
methods usually adopt the decoupled encoder-decoder paradigm, where the encoder …