PEPR reframes learning with privileged event data as predicting latent event features from RGB to improve domain generalization in object detection and segmentation without direct cross-modal alignment.
The cityscapes dataset for semantic urban scene understanding
3 Pith papers cite this work. Polarity classification is still indexing.
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SigLino distills SigLIP2 and DINOv3 into efficient vision models via asymmetric relation-knowledge distillation, token-balanced batching, and hierarchical data sampling on a new 200M-image corpus, yielding better transfer to grounding VLMs than training from scratch.
The approach uses the analytic solution of distribution discrepancy consistency within categories as semantic maps, eliminating training and model-specific modulation while claiming state-of-the-art results on eight benchmarks.
citing papers explorer
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PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
PEPR reframes learning with privileged event data as predicting latent event features from RGB to improve domain generalization in object detection and segmentation without direct cross-modal alignment.
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SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models
SigLino distills SigLIP2 and DINOv3 into efficient vision models via asymmetric relation-knowledge distillation, token-balanced batching, and hierarchical data sampling on a new 200M-image corpus, yielding better transfer to grounding VLMs than training from scratch.
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Direct Segmentation without Logits Optimization for Training-Free Open-Vocabulary Semantic Segmentation
The approach uses the analytic solution of distribution discrepancy consistency within categories as semantic maps, eliminating training and model-specific modulation while claiming state-of-the-art results on eight benchmarks.