我们呈现恐惧，新颖，快速，高效，准确，强大的暹罗视觉跟踪器。我们介绍了对象模型适配的架构块，称为双模板表示，以及像素 - 明智的融合块，以实现模型的额外灵活性和效率。双模板模块仅包含单个学习参数的时间信息，而像素-Wise融合块与标准相关模块相比，像素-Wise融合块对具有较少参数的判别特征进行了更多的辨别特征。通过用新型模块插入复杂的骨干，恐惧-M和恐惧-L跟踪器在既准确性和效率的几个学术基准上超过大多数暹粒例子。使用轻质骨干，优化的版本恐惧-XS提供了超过10倍的跟踪跟踪，而不是当前暹罗跟踪器，同时保持最先进的结果。 GEAF-XS跟踪器比LightTrack [62]更小2.4倍，比LightTrack [62]更高。此外，我们通过在能量消耗和执行速度上引入基准来扩展模型效率的定义。源代码，预先训练的模型和评估协议将根据要求提供

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a doublescale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plugin of sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-ofthe-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides, we show the architecture to be effective for general image restoration tasks too. Our codes, models and data are available at: https: //github.com/KupynOrest/DeblurGANv2.

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -object detection on (de-)blurred images. The method is 5 times faster than the closest competitor -Deep-Deblur [25]. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation.The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGAN