RGB D Saliency Detection

RGB-D saliency detection aims to identify visually prominent regions in images and videos by combining color (RGB) and depth information. Current research focuses on developing robust fusion methods for these modalities, often employing multi-scale convolutional neural networks (CNNs) or transformer-based architectures to effectively integrate features at various levels and scales, addressing challenges like noisy or misaligned depth data. These advancements improve the accuracy and efficiency of saliency prediction, impacting applications such as autonomous driving, robotics, and image editing by enabling more accurate scene understanding and object segmentation. The development of large, annotated datasets is also a key area of focus to further improve model performance.