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
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Exact Linear Attention uses kernel decomposition for exact linear-complexity attention in Transformers, with proposed kernels addressing gradient and dilution issues plus new modules for memory and MoE.
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%.
Faster RCNN is extended with a track branch and trained end-to-end on concatenated video frames to unify detection and re-identification, reaching 57.79% mAP on the AIC19 vehicle dataset.
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A unified neural network for object detection, multiple object tracking and vehicle re-identification
Faster RCNN is extended with a track branch and trained end-to-end on concatenated video frames to unify detection and re-identification, reaching 57.79% mAP on the AIC19 vehicle dataset.