Saliency Ranking

Saliency ranking aims to order the importance of multiple salient objects within an image or video, going beyond simply identifying salient regions. Current research focuses on improving the accuracy and robustness of ranking algorithms, often employing deep learning architectures like transformers and incorporating contextual information (e.g., object-object and object-scene relationships) to better mimic human visual attention. These advancements are crucial for improving the performance of downstream tasks such as object detection and video moment retrieval, where the relative importance of objects significantly impacts accuracy.