Due to the extremely low signal-to-noise ratio (SNR) and unknown poses
(...
Pre-trained language models (PLMs) have played an increasing role in
mul...
We propose a twin support vector quantile regression (TSVQR) to capture ...
In the past decade, Artificial Intelligence (AI) algorithms have made
pr...
OOD-CV challenge is an out-of-distribution generalization task. To solve...
OOD-CV challenge is an out-of-distribution generalization task. In this
...
Machine learning-based segmentation in medical imaging is widely used in...
Convolutional neural networks (CNNs) have demonstrated gratifying result...
Universal domain adaptive object detection (UniDAOD)is more challenging ...
Vanilla unsupervised domain adaptation methods tend to optimize the mode...
Semi-supervised object detection has made significant progress with the
...
Self-training for unsupervised domain adaptive object detection is a
cha...
Inspired by the remarkable zero-shot generalization capacity of
vision-l...
Convolutional neural networks (CNNs) have achieved significant success i...
Domain generalization (DG) is a fundamental yet very challenging researc...
Simile interpretation (SI) and simile generation (SG) are challenging ta...
Conventional domain generalization aims to learn domain invariant
repres...
In this paper, we consider the linear programming (LP) formulation for d...
It is a strong prerequisite to access source data freely in many existin...
False positive is one of the most serious problems brought by agnostic d...
Unsupervised domain adaptation (UDA) assumes that source and target doma...
Deep clustering against self-supervised learning is a very important and...
Layer assignment is seldom picked out as an independent research topic i...
In rank aggregation, preferences from different users are summarized int...
Shift operation is an efficient alternative over depthwise separable
con...
Neuron pruning is an efficient method to compress the network into a sli...