RELO formulates visual object tracking localization as a Markov decision process solved by reinforcement learning with combined IoU and AUC rewards, augmented by layer-aligned temporal token propagation, and reports 57.5% AUC on LaSOText without template updates.
arXiv preprint arXiv:1708.04896 (2017)
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abstract
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: https://github.com/zhunzhong07/Random-Erasing.
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UNVERDICTED 4representative citing papers
A teacher-student model with co-saliency network and growing-probability occlusion simulator outperforms prior methods on four occluded person re-identification benchmarks.
Rényi entropy of attention maps serves as a tunable criterion for pruning redundant patches in vision transformers, reducing compute with preserved accuracy on image recognition.
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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RELO: Reinforcement Learning to Localize for Visual Object Tracking
RELO formulates visual object tracking localization as a Markov decision process solved by reinforcement learning with combined IoU and AUC rewards, augmented by layer-aligned temporal token propagation, and reports 57.5% AUC on LaSOText without template updates.
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A Novel Teacher-Student Learning Framework For Occluded Person Re-Identification
A teacher-student model with co-saliency network and growing-probability occlusion simulator outperforms prior methods on four occluded person re-identification benchmarks.
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R\'enyi Attention Entropy for Patch Pruning
Rényi entropy of attention maps serves as a tunable criterion for pruning redundant patches in vision transformers, reducing compute with preserved accuracy on image recognition.
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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.