6 accepted papers
In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VFMs), which improves in-domain results but harms generalization, or (ii) combines
Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried
Referring Image Segmentation, the task of finding and segmenting objects in an image conditioned on a natural language description, is crucial for human-robot collaboration. However, current RIS methods often implement visual-text alignment relying on computationally intensive Transformer-based self
Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip. Existing methods restrict reasoning either to independent short clips, losing global context, or process the entire video offline, impairing their application in a streaming fashio…
Few-shot segmentation aims to segment unseen categories from just a handful of annotated examples. This requires mechanisms to identify semantically related objects across images and accurately produce masks. We note that Segment Anything 2 (SAM2), with its prompt-and-propagate mechanism, provides s…
Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility they obtain outstanding performance in multiple segmentation tasks such as semantic and panoptic under a single unified framework. To achieve such impressive performan…