Visual-based perception is the key module for autonomous driving. Among those
visual perception tasks, video object detection is a primary yet challenging
one because of feature degradation caused by fast motion or multiple poses.
Current models usually aggregate features from the neighboring frames to
enhance the object representations for the task heads to generate more accurate
predictions. Though getting better performance, these methods rely on the
information from the future frames and suffer from high computational
complexity. Meanwhile, the aggregation process is not reconfigurable during the
inference time. These issues make most of the existing models infeasible for
online applications. To solve these problems, we introduce a stepwise spatial
global-local aggregation network. Our proposed models mainly contain three
parts: 1). Multi-stage stepwise network gradually refines the predictions and
object representations from the previous stage; 2). Spatial global-local
aggregation fuses the local information from the neighboring frames and global
semantics from the current frame to eliminate the feature degradation; 3).
Dynamic aggregation strategy stops the aggregation process early based on the
refinement results to remove redundancy and improve efficiency. Extensive
experiments on the ImageNet VID benchmark validate the effectiveness and
efficiency of our proposed models.

我们提出了一种分阶段空间全局 - 局部聚合网络，用于解决快速运动或多个姿势引起的特征退化问题，并提高在在线应用中的效率。

SSGA-Net: 逐步空间全局本地聚合网络用于自动驾驶

SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for  Autonomous Driving

Photometric calibration is essential to many computer vision applications.
One of its key benefits is enhancing the performance of Visual SLAM, especially
when it depends on a direct method for tracking, such as the standard KLT
algorithm. Another advantage could be in retrieving the sensor irradiance
values from measured intensities, as a pre-processing step for some vision
algorithms, such as shape-from-shading. Current photometric calibration systems
rely on a joint optimization problem and encounter an ambiguity in the
estimates, which can only be resolved using ground truth information. We
propose a novel method that solves for photometric parameters using a
sequential estimation approach. Our proposed method achieves high accuracy in
estimating all parameters; furthermore, the formulations are linear and convex,
which makes the solution fast and suitable for online applications. Experiments
on a Visual Odometry system validate the proposed method and demonstrate its
advantages.

本研究提出了一种新的序贯估计方法来解决光度参数的校准问题，该方法在 Photometric Calibration 中具有高精度，解决了现有方法的优化问题。实验证明，这种方法适用于直接法和在线应用。

OSPC：在线序列光度校准

OSPC: Online Sequential Photometric Calibration

Current state-of-the-art action detection systems are tailored for offline
batch-processing applications. However, for online applications like
human-robot interaction, current systems fall short, either because they only
detect one action per video, or because they assume that the entire video is
available ahead of time. In this work, we introduce a real-time and online
joint-labelling and association algorithm for action detection that can
incrementally construct space-time action tubes on the most challenging action
videos in which different action categories occur concurrently. In contrast to
previous methods, we solve the detection-window association and action
labelling problems jointly in a single pass. We demonstrate superior online
association accuracy and speed (2.2ms per frame) as compared to the current
state-of-the-art offline systems. We further demonstrate that the entire action
detection pipeline can easily be made to work effectively in real-time using
our action tube construction algorithm.

本文介绍了一种实时、在线的联合标注和关联算法，用于动作检测，可以增量构建空间 - 时间动作管，在最具挑战性的动作视频中，可以同时发生不同的动作类别，其实时关联准确度和速度优于当前最先进的离线系统。