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cs.CV2026

Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos

Mohamed Afham, Christoph Reich, Oliver Hahn +2

Procedure planning seeks to estimate a sequence of actions to transition from an observed initial state to a given goal state. Current procedure planning approaches directly predic…

cs.CV2026

Scene-Centric Unsupervised Video Panoptic Segmentation

Christoph Reich, Oliver Hahn, Nikita Araslanov +4

Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task se…

cs.CV2026

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…

cs.CV2026

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)…

cs.CV2025

Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery

Xinrui Gong, Oliver Hahn, Christoph Reich +4

Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage o…

cs.CV2025

Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion

Aleksandar Jevtić, Christoph Reich, Felix Wimbauer +4

Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive gr…