LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
FCOS: fully convolutional one-stage object detection
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HeroCrystal achieves 33.4% mAP on cross-domain multi-camera object detection by combining one-shot diffusion-based synthetic data generation, probabilistic federated Faster R-CNN, and inconsistent-category distillation, outperforming prior privacy-preserving baselines by 2.1%.
citing papers explorer
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LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
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Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation
HeroCrystal achieves 33.4% mAP on cross-domain multi-camera object detection by combining one-shot diffusion-based synthetic data generation, probabilistic federated Faster R-CNN, and inconsistent-category distillation, outperforming prior privacy-preserving baselines by 2.1%.