Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Co-saliency detection is a newly emerging and rapidly growing research area in the
computer vision community. As a novel branch of visual saliency, co-saliency detection …

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Salient object detection from RGB-D images is an important yet challenging vision task,
which aims at detecting the most distinctive objects in a scene by combining color …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

As an interesting and emerging topic, co-saliency detection aims at simultaneously
extracting common salient objects from a group of images. On one hand, traditional co …

In this article, we conduct a comprehensive study on the co-salient object detection (CoSOD)
problem for images. CoSOD is an emerging and rapidly growing extension of salient object …

While recent progress has significantly boosted few-shot classification (FSC) performance,
few-shot object detection (FSOD) remains challenging for modern learning systems. Existing …

Co-saliency detection, which focuses on extracting commonly salient objects in a group of
relevant images, has been attracting research interest because of its broad applications. In …