Image Difference Grounding with Natural Language

Visual grounding (VG) typically focuses on locating regions of interest within an image using natural language, and most existing VG methods are limited to single-image interpretations. This limits their applicability in real-world scenarios like automatic surveillance, where detecting subtle but meaningful visual differences across multiple images is crucial. Besides, previous work on image difference understanding (IDU) has either focused on detecting all change regions without cross-modal text guidance, or on providing coarse-grained descriptions of differences. Therefore, to push towards finer-grained vision-language perception, we propose Image Difference Grounding (IDG), a task designed to precisely localize visual differences based on user instructions. We introduce DiffGround, a large-scale and high-quality dataset for IDG, containing image pairs with diverse visual variations along with instructions querying fine-grained differences. Besides, we present a baseline model for IDG, DiffTracker, which effectively integrates feature differential enhancement and common suppression to precisely locate differences. Experiments on the DiffGround dataset highlight the importance of our IDG dataset in enabling finer-grained IDU. To foster future research, both DiffGround data and DiffTracker model will be publicly released.