MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation

arXiv:2609.39681 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/martinovicivan/MC-PanDA

Terminology

• Benchmark — 46× in this paper · explained in 2 episodes
• Transformer — 39× in this paper · explained in 2 episodes
• Adaptation — 36× in this paper · explained in 1 episode
• Consistency — 34× in this paper · explained in 3 episodes
• Robustness — 21× in this paper · explained in 5 episodes
• Domain Adaptation — 19× in this paper · explained in 5 episodes
• Mask-wide Loss Scaling — 16× in this paper
• Pseudo-labels — 14× in this paper · explained in 2 episodes
• Aggregation — 13× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Embeddings — 8× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Domain Shift — 7× in this paper · explained in 4 episodes
• Confidence-based Point Filtering — 6× in this paper
• Ablation study — 5× in this paper · explained in 1 episode
• Aggregation Function — 4× in this paper · explained in 1 episode
• Distillation — 4× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• Validation Panoptic Quality — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Learning to Fuse Things and Stuff
• DINOv3
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models