Semantic-Centric Alignment for Zero-shot Panoptic Segmentation with Limited Data

2026/02/16 by Jialei Chen, Daisuke Deguchi, D. LI +6
Computer Science · #Domain Adaptation and Few-Shot Learning #Advanced Neural Network Applications #Multimodal Machine Learning Applications

paper · doi:10.1007/s11263-025-02648-4

Citations

• AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing
• OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
• Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
• PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
• OMG-Seg: Is One Model Good Enough For All Segmentation?
• Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation
• Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
• Micrograph segmentations for DDEVD
• Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
• Side Adapter Network for Open-Vocabulary Semantic Segmentation
• ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
• SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
• Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
• Temporal Transductive Inference for Few-Shot Video Object Segmentation
• A ConvNet for the 2020s
• Language-driven Semantic Segmentation
• Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
• Decoupling Zero-Shot Semantic Segmentation
• Masked-attention Mask Transformer for Universal Image Segmentation
• Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation
• Per-Pixel Classification is Not All You Need for Semantic Segmentation
• Emerging Properties in Self-Supervised Vision Transformers
• Fully Convolutional Networks for Panoptic Segmentation
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Denoising Diffusion Probabilistic Models
• Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition
• End-to-End Object Detection with Transformers
• Conditional Convolutions for Instance Segmentation
• Connecting Vision and Language with Localized Narratives
• Momentum Contrast for Unsupervised Visual Representation Learning
• CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language
• Zero-Shot Semantic Segmentation
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• COCO-Stuff: Thing and Stuff Classes in Context
• Open-Vocabulary Universal Image Segmentation with MaskCLIP