We are living in a three-dimensional space while moving forward through a fourth
dimension: time. To allow artificial intelligence to develop a comprehensive understanding …

Despite the progress of image segmentation for accurate visual entity segmentation,
completing the diverse requirements of image editing applications for different-level region …

Scene graph generation has emerged as a prominent research field in computer vision
witnessing significant advancements in the recent years. However despite these strides …

Scene graph generation aims to capture detailed spatial and semantic relationships
between objects in an image which is challenging due to incomplete labeling long-tailed …

Panoptic Scene Graph Generation (PSG) aims at achieving a comprehensive image
understanding by simultaneously segmenting objects and predicting relations among …

Referring video object segmentation (RVOS) aims to segment the target instance referred by
a given text expression in a video clip. The text expression normally contains sophisticated …

Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their
relations, enabling the structured understanding of an image. Previous methods focus on …

Panoptic Scene Graph Generation (PSG) aims to generate a comprehensive graph-structure
representation based on panoptic segmentation masks. Despite remarkable progress in …

In this section, we first provide a summary highlighting the key contributions of our method,
along with comparisons to [4] and [7]. Following this, we offer an additional comparison with …

Dense video captioning tasks are used to detect interesting events and provide descriptive
text for these events from untrimmed videos. This technology has the potential to be used in …