Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

In recent years, remote sensing target recognition algorithms based on deep learning
technology have gradually become mainstream in the field of remote sensing because of the …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Depression stands as one of the most widespread psychological disorders and has
garnered increasing attention. Currently, how to effectively achieve automatic multimodal …

Estimating the pose of an uncooperative spacecraft is an important computer vision problem
for enabling the deployment of automatic vision-based systems in orbit, with applications …

Abstract We introduce Meta-AdaM, a meta-learned adaptive optimizer with momentum,
designed for few-shot learning tasks that pose significant challenges to deep learning …

Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …

Static analysis is a widely used technique in software engineering for identifying and
mitigating bugs. However, a significant hurdle lies in achieving a delicate balance between …

The development of large vision-language models notably CLIP has catalyzed research into
effective adaptation techniques with a particular focus on soft prompt tuning. Conjointly test …

Environmental changes can profoundly impact the performance of artificial intelligence
systems operating in the real world, with effects ranging from overt catastrophic failures to …