Autonomous driving systems need to handle complex scenarios such as lane
following, avoiding collisions, taking turns, and responding to traffic
signals. In recent years, approaches based on end-to-end behavioral cloning
have demonstrated remarkable performance in point-to-point navigational
scenarios, using a realistic simulator and standard benchmarks. Offline
imitation learning is readily available, as it does not require expensive hand
annotation or interaction with the target environment, but it is difficult to
obtain a reliable system. In addition, existing methods have not specifically
addressed the learning of reaction for traffic lights, which are a rare
occurrence in the training datasets. Inspired by the previous work on
multi-task learning and attention modeling, we propose a novel multi-task
attention-aware network in the conditional imitation learning (CIL) framework.
This does not only improve the success rate of standard benchmarks, but also
the ability to react to traffic lights, which we show with standard benchmarks.

本文提出了一种基于多任务学习和注意力模型的新型自动驾驶条件模仿学习网络，通过解决交通灯反应问题，不仅提高了标准基准测试的成功率，而且具备了响应交通信号的能力。

面向端到端自动驾驶的多任务学习与注意力机制

Multi-task Learning with Attention for End-to-end Autonomous Driving

We describe a latent approach that learns to detect actions in long sequences
given training videos with only whole-video class labels. Our approach makes
use of two innovations to attention-modeling in weakly-supervised learning.
First, and most notably, our framework uses an attention model to extract both
foreground and background frames whose appearance is explicitly modeled. Most
prior works ignore the background, but we show that modeling it allows our
system to learn a richer notion of actions and their temporal extents. Second,
we combine bottom-up, class-agnostic attention modules with top-down,
class-specific activation maps, using the latter as form of self-supervision
for the former. Doing so allows our model to learn a more accurate model of
attention without explicit temporal supervision. These modifications lead to
10% AP@IoU=0.5 improvement over existing systems on THUMOS14. Our proposed
weaklysupervised system outperforms recent state-of-the-arts by at least 4.3%
AP@IoU=0.5. Finally, we demonstrate that weakly-supervised learning can be used
to aggressively scale-up learning to in-the-wild, uncurated Instagram videos.
The addition of these videos significantly improves localization performance of
our weakly-supervised model

该研究提出了一种潜在方法，利用注意力模型进行弱监督学习，其可以检测出影片中的动作，而无需特定类别的标签， 并利用弱监督学习进行比较准确的学习和定位，成功地应用于 Instagram 中的不加筛选的视频之间。

基于背景建模的弱监督动作定位

Weakly-supervised Action Localization with Background Modeling

Recent developments in gradient-based attention modeling have seen attention
maps emerge as a powerful tool for interpreting convolutional neural networks.
Despite good localization for an individual class of interest, these techniques
produce attention maps with substantially overlapping responses among different
classes, leading to the problem of visual confusion and the need for
discriminative attention. In this paper, we address this problem by means of a
new framework that makes class-discriminative attention a principled part of
the learning process. Our key innovations include new learning objectives for
attention separability and cross-layer consistency, which result in improved
attention discriminability and reduced visual confusion. Extensive experiments
on image classification benchmarks show the effectiveness of our approach in
terms of improved classification accuracy, including CIFAR-100 (+3.33%),
Caltech-256 (+1.64%), ILSVRC2012 (+0.92%), CUB-200-2011 (+4.8%) and PASCAL
VOC2012 (+5.73%).

提出一个新的框架，通过学习过程将分类判别的注意力作为其中的一个基本部分，实现对注意力的分离和跨层一致性控制，有效地提高了图像分类的准确度，包括提高了 CIFAR-100 (+3.33%)、Caltech-256 (+1.64%)、ILSVRC2012 (+0.92%)、CUB-200-2011 (+4.8%) 和 PASCAL VOC2012 (+5.73%) 等基准数据集的分类准确度。