This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

摘要占主导地位的序列转导模型基于复杂的递归或卷积神经网络, 包括编码器和解码器.
性能最好的模型还通过注意力机制连接编码器和解码器. 我们提出了一种新的简单网络架构 …

Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …

Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

In this paper, we present a new sequence-to-sequence learning framework for visual
tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem …

For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …

Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …