11 papers
PrAda: Few-Shot Visual Adaptation for Text-Prompted Segmentation
Gabriele Rosi, Fabio Cermelli, Carlo Masone +1
Segmenting images is critical for visual understanding but demands extensive pixel-level annotations. Foundational models have enabled new paradigms for predicting new classes guid…
MARCO: Navigating the Unseen Space of Semantic Correspondence
Claudia Cuttano, Gabriele Trivigno, Carlo Masone +1
Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize…
INSID3: Training-Free In-Context Segmentation with DINOv3
Claudia Cuttano, Gabriele Trivigno, Christoph Reich +3
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)…
SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation
Claudia Cuttano, Gabriele Trivigno, Giuseppe Averta +1
Few-shot segmentation aims to segment unseen object categories from just a handful of annotated examples. This requires mechanisms that can both identify semantically related objec…
Towards Safer and Understandable Driver Intention Prediction
Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai +2
Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomo…
Road Obstacle Video Segmentation
Shyam Nandan Rai, Shyamgopal Karthik, Mariana-Iuliana Georgescu +3
With the growing deployment of autonomous driving agents, the detection and segmentation of road obstacles have become critical to ensure safe autonomous navigation. However, exist…