Many structured prediction tasks in machine vision have a collection of
acceptable answers, instead of one definitive ground truth answer. Segmentation
of images, for example, is subject to human labeling bias. Similarly, there are
multiple possible pixel values that could plausibly complete occluded image
regions. State-of-the art supervised learning methods are typically optimized
to make a single test-time prediction for each query, failing to find other
modes in the output space. Existing methods that allow for sampling often
sacrifice speed or accuracy.
We introduce a simple method for training a neural network, which enables
diverse structured predictions to be made for each test-time query. For a
single input, we learn to predict a range of possible answers. We compare
favorably to methods that seek diversity through an ensemble of networks. Such
stochastic multiple choice learning faces mode collapse, where one or more
ensemble members fail to receive any training signal. Our best performing
solution can be deployed for various tasks, and just involves small
modifications to the existing single-mode architecture, loss function, and
training regime. We demonstrate that our method results in quantitative
improvements across three challenging tasks: 2D image completion, 3D volume
estimation, and flow prediction.

本文介绍了一种简单的方法来训练神经网络，使得每个测试查询时间内能够进行多元化的结构性预测，同时比现有方法具有更好的多样性和速度。在 2D 图像填充，3D 体积估计和流场预测等三个具有挑战性的任务中，我们的方法得到了量化的改进。

DiverseNet：当一个正确答案不够时

DiverseNet: When One Right Answer is not Enough

Trajectory prediction is crucial for autonomous vehicles. The planning system
not only needs to know the current state of the surrounding objects but also
their possible states in the future. As for vehicles, their trajectories are
significantly influenced by the lane geometry and how to effectively use the
lane information is of active interest. Most of the existing works use
rasterized maps to explore road information, which does not distinguish
different lanes. In this paper, we propose a novel instance-aware
representation for lane representation. By integrating the lane features and
trajectory features, a goal-oriented lane attention module is proposed to
predict the future locations of the vehicle. We show that the proposed lane
representation together with the lane attention module can be integrated into
the widely used encoder-decoder framework to generate diverse predictions. Most
importantly, each generated trajectory is associated with a probability to
handle the uncertainty. Our method does not suffer from collapsing to one
behavior modal and can cover diverse possibilities. Extensive experiments and
ablation studies on the benchmark datasets corroborate the effectiveness of our
proposed method. Notably, our proposed method ranks third place in the
Argoverse motion forecasting competition at NeurIPS 2019.

本文提出一种基于实例感知的车道表示法和目标導向车道注意力模型，结合车道特征和车辆轨迹特征进行车辆位置预测，并在 NeurIPS 2019 的 Argoverse 运动预测竞赛中获得第三名，实验证明了模型的有效性。