Detection and localization of regions of images that attract immediate human visual attention
is currently an intensive area of research in computer vision. The capability of automatic …

Large vision-language models, such as CLIP, learn robust representations of text and
images, facilitating advances in many downstream tasks, including zero-shot classification …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

We develop an approach to learning visual representations that embraces multimodal data,
driven by a combination of intra-and inter-modal similarity preservation objectives. Unlike …

We provide a comprehensive evaluation of salient object detection (SOD) models. Our
analysis identifies a serious design bias of existing SOD datasets which assumes that each …

We present a framework for efficient inference in structured image models that explicitly
reason about objects. We achieve this by performing probabilistic inference using a …

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29
salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven …

Image saliency detection has recently witnessed rapid progress due to deep convolutional
neural networks. However, none of the existing methods is able to identify object instances …

Salient object detection (SOD) aims to identify standout elements in a scene, with recent
advancements primarily focused on integrating depth data (RGB-D) or temporal data from …

Common object counting in a natural scene is a challenging problem in computer vision with
numerous real-world applications. Existing image-level supervised common object counting …