Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

Abstract Within Convolutional Neural Network (CNN), the convolution operations are good
at extracting local features but experience difficulty to capture global representations. Within …

We introduce a new generative model where samples are produced via Langevin dynamics
using gradients of the data distribution estimated with score matching. Because gradients …

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing
photorealistic images given an input semantic layout. Previous methods directly feed the …

Abstract In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial
neurons in each layer are designed to share the same size. It is well-known in the …