文件名称:
Learning Lightweight Lane Detection CNNs by Self Attention Distillation.pdf
开发工具:
文件大小: 3mb
下载次数: 0
上传时间: 2020-04-20
详细说明：Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals in- herent in lane annotations. Without learning from much richer context, these models often fail in challenging sce- narios, e.g., severe occlusion, ambiguous lanes, and poor lighting conditions. In this paper, we present a novel knowl- edge distillation approach, i.e., Self Attention Distillation (SAD), which allows a model to learn from itself and gains substantial improvement without any additional supervision or labels. Specifically, we observe that attention maps ex- tracted from a model trained to a reasonable level would encode rich contextual information. The valuable contex- tual information can be used as a form of ‘free’ supervision for further representation learning through performing top- down and layer-wise attention distillation within the net- work itself. SAD can be easily incorporated in any feed- forward convolutional neural networks (CNN) and does not increase the inference time. We validate SAD on three pop- ular lane detection benchmarks (TuSimple, CULane and BDD100K) using lightweight models such as ENet, ResNet- 18 and ResNet-34. The lightest model, ENet-SAD, per- forms comparatively or even surpasses existing algorithms. Notably, ENet-SAD has 20 × fewer parameters and runs 10 × faster compared to the state-of-the-art SCNN [16], while still achieving compelling performance in all bench- marks. Our code is available at https://github. com/cardwing/Codes-for-Lane-Detection.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
