Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bias towards seen classes. Taking the former problem into account, our method compares the support feature map with the query feature map at multi scales to become scale-agnostic. As a solution to the latter problem, a supervised model, called as base learner, is trained on available classes to accurately identify pixels belonging to seen classes. Hence, subsequent meta learner has a chance to discard areas belonging to seen classes with the help of an ensemble learning model that coordinates meta learner with the base learner. We simultaneously address these two vital problems for the first time and achieve state-of-the-art performances on both PASCAL-5i and COCO-20i datasets.

在本文中，使用Resnet-34作为功能提取器，将基于LSTM的基于LSTM自动编码器的体系结构用于嗜睡。该问题被认为是单个受试者的异常检测。因此，只有普通的驾驶表示形式，并且可以根据网络的知识来区分嗜睡表征，从而产生更高的重建损失。在我们的研究中，通过标签分配的方法研究了正常和异常夹的置信度水平，以便根据不同的置信率分析LSTM自动编码器的训练性能以及测试过程中遇到的异常情况的解释。我们的方法在NTHU-DDD上进行了实验，并通过最先进的异常检测方法进行基准测试，以使驱动器嗜睡。结果表明，所提出的模型在曲线（AUC）下达到0.8740面积的检测率，并能够在某些情况下提供重大改进。

We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.