DiffClean applies text-guided diffusion to erase makeup from faces, boosting age estimation and verification accuracy over makeup-affected images.
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are immensely popular in embedded vision applications. This paper brings single-shot detectors up to the same level as current two-stage techniques. We do this by improving training for the state-of-the-art single-shot detector, RetinaNet, in three ways: integrating instance mask prediction for the first time, making the loss function adaptive and more stable, and including additional hard examples in training. We call the resulting augmented network RetinaMask. The detection component of RetinaMask has the same computational cost as the original RetinaNet, but is more accurate. COCO test-dev results are up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the runtime is the same during evaluation. Adding Group Normalization increases the performance of RetinaMask-101 to 41.7 mAP. Code is at:https://github.com/chengyangfu/retinamask
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A two-stage pipeline generates pseudo masks from image-level labels to train Mask R-CNN, achieving state-of-the-art results on PASCAL VOC 2012 for weakly supervised instance segmentation.
YOLOv4 achieves 43.5% AP (65.7% AP50) on MS COCO at ~65 FPS on Tesla V100 by integrating WRC, CSP, CmBN, SAT, Mish activation, Mosaic augmentation, DropBlock, and CIoU loss.
citing papers explorer
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DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
DiffClean applies text-guided diffusion to erase makeup from faces, boosting age estimation and verification accuracy over makeup-affected images.
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Where are the Masks: Instance Segmentation with Image-level Supervision
A two-stage pipeline generates pseudo masks from image-level labels to train Mask R-CNN, achieving state-of-the-art results on PASCAL VOC 2012 for weakly supervised instance segmentation.
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YOLOv4: Optimal Speed and Accuracy of Object Detection
YOLOv4 achieves 43.5% AP (65.7% AP50) on MS COCO at ~65 FPS on Tesla V100 by integrating WRC, CSP, CmBN, SAT, Mish activation, Mosaic augmentation, DropBlock, and CIoU loss.