In computer vision, object detection is the classical and most challenging problem to get
accurate results in detecting objects. With the significant advancement of deep learning …

The combination of infrared and visible videos aims to gather more comprehensive feature
information from multiple sources and reach superior results on various practical tasks, such …

Instance segmentation in videos, which aims to segment and track multiple objects in video
frames, has garnered a flurry of research attention in recent years. In this paper, we present …

Fine-tuning large vision-language models is a challenging task. Prompt tuning approaches
have been introduced to learn fixed textual or visual prompts while freezing the pre-trained …

Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen
classes by resorting to a small set of support images that contain pixel-level object …

Camouflaged object detection (COD) is an important yet challenging task, with great
application values in industrial defect detection, medical care, etc. The challenges mainly …

Deepfake technology, utilizing deep learning and computer vision, presents significant
security threats by generating highly realistic synthetic media, such as images and videos. In …

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light
of the widespread adoption of vision-based foundational models. Its primary objective is to …

Video semantic role grounding has gained substantial interest from both the academic and
industrial communities. While existing methods have demonstrated considerable …

The advancement of computer vision has pushed visual analysis tasks from still images to
the video domain. In recent years, video instance segmentation, which aims to track and …