StableMTL repurposes latent diffusion models for multi-task learning from partially annotated synthetic data via unified latent loss, task encoding, and a multi-stream task-attention architecture, reporting outperformance on 7 tasks across 8 benchmarks.
In: CVPR (2016)
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MILE combines incremental LoRA experts with prototype-guided gating to support continual semantic segmentation across domains and modalities while adding only a small number of parameters per task.
A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.
citing papers explorer
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StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets
StableMTL repurposes latent diffusion models for multi-task learning from partially annotated synthetic data via unified latent loss, task encoding, and a multi-stream task-attention architecture, reporting outperformance on 7 tasks across 8 benchmarks.
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MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
MILE combines incremental LoRA experts with prototype-guided gating to support continual semantic segmentation across domains and modalities while adding only a small number of parameters per task.
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Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
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Dual-Foundation Models for Unsupervised Domain Adaptation
Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.