This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

The task of multi-label image recognition is to predict a set of object labels that present in an
image. As objects normally co-occur in an image, it is desirable to model the label …

This paper presents a simple and effective approach to solving the multi-label classification
problem. The proposed approach leverages Transformer decoders to query the existence of …

Multi-label image recognition is a challenging computer vision task of practical use.
Progresses in this area, however, are often characterized by complicated methods, heavy …

Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven
notoriously difficult to understand how they reach their decisions. We here introduce a high …

Early detection of arrhythmia and effective treatment can prevent deaths caused by
cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the …

In this paper, we tackle the domain adaptive object detection problem, where the main
challenge lies in significant domain gaps between source and target domains. Previous …

Despite remarkable progress, weakly supervised segmentation methods are still inferior to
their fully supervised counterparts. We obverse that the performance gap mainly comes from …

In this work, we propose Adversarial Complementary Learning (ACoL) to automatically
localize integral objects of semantic interest with weak supervision. We first mathematically …