Offline reinforcement learning (RL) algorithms are applied to learn
performant, well-generalizing policies when provided with a static dataset of
interactions. Many recent approaches to offline RL have seen substantial
success, but with one key caveat: they demand substantial per-dataset
hyperparameter tuning to achieve reported performance, which requires policy
rollouts in the environment to evaluate; this can rapidly become cumbersome.
Furthermore, substantial tuning requirements can hamper the adoption of these
algorithms in practical domains. In this paper, we present TD3 with Behavioral
Supervisor Tuning (TD3-BST), an algorithm that trains an uncertainty model and
uses it to guide the policy to select actions within the dataset support.
TD3-BST can learn more effective policies from offline datasets compared to
previous methods and achieves the best performance across challenging
benchmarks without requiring per-dataset tuning.

TD3-BST 是一种应用于脱机强化学习算法的不确定性模型，通过指导策略在数据集支持中选择动作，从而比先前的方法更有效地从离线数据集中学习策略，并在具有挑战性的基准测试中取得最佳性能，无需进行特定数据集的调整。

行为监督调节的离线强化学习

Offline Reinforcement Learning with Behavioral Supervisor Tuning

Event cameras are ideally suited to capture HDR visual information without
blur but perform poorly on static or slowly changing scenes. Conversely,
conventional image sensors measure absolute intensity of slowly changing scenes
effectively but do poorly on high dynamic range or quickly changing scenes. In
this paper, we present an event-based video reconstruction pipeline for High
Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame
augmentation pre-processing step that deblurs and temporally interpolates frame
data using events. The augmented frame and event data are then fused using a
novel asynchronous Kalman filter under a unifying uncertainty model for both
sensors. Our experimental results are evaluated on both publicly available
datasets with challenging lighting conditions and fast motions and our new
dataset with HDR reference. The proposed algorithm outperforms state-of-the-art
methods in both absolute intensity error (48% reduction) and image similarity
indexes (average 11% improvement).

本文提出了一种基于事件相机的高动态范围视频重建方法，该方法包括模糊去除、时间插值、异步 Kalman 滤波等步骤，可以较好地处理 HDR 视频，实验结果验证了本方法的优越性。

混合事件相机的异步卡尔曼滤波器

An Asynchronous Kalman Filter for Hybrid Event Cameras

We tackle the fundamentally ill-posed problem of 3D human localization from
monocular RGB images. Driven by the limitation of neural networks outputting
point estimates, we address the ambiguity in the task by predicting confidence
intervals through a loss function based on the Laplace distribution. Our
architecture is a light-weight feed-forward neural network that predicts 3D
locations and corresponding confidence intervals given 2D human poses. The
design is particularly well suited for small training data, cross-dataset
generalization, and real-time applications. Our experiments show that we (i)
outperform state-of-the-art results on KITTI and nuScenes datasets, (ii) even
outperform a stereo-based method for far-away pedestrians, and (iii) estimate
meaningful confidence intervals. We further share insights on our model of
uncertainty in cases of limited observations and out-of-distribution samples.

通过使用 Laplace 分布的损失函数，预测置信区间，设计了一种轻量级前馈神经网络，可以从 2D 人体姿势中预测出 3D 位置和对应的置信区间。该方法不仅在 KITTI 和 nuScenes 数据集上超过了最先进的结果，而且在远处行人的情况下，甚至超过了基于立体视觉的方法，并且可以估算有意义的置信区间。

MonoLoco: 单目 3D 行人定位与不确定性估计

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty  Estimation

Unconstrained video-based face recognition is a challenging problem due to
significant within-video variations caused by pose, occlusion and blur. To
tackle this problem, an effective idea is to propagate the identity from
high-quality faces to low-quality ones through contextual connections, which
are constructed based on context such as body appearance. However, previous
methods have often propagated erroneous information due to lack of uncertainty
modeling of the noisy contextual connections. In this paper, we propose the
Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation
between tracklets, which are represented by nodes in a graph. UGG explicitly
models the uncertainty of the contextual connections by adaptively updating the
weights of the edge gates according to the identity distributions of the nodes
during inference. UGG is a generic graphical model that can be applied at only
inference time or with end-to-end training. We demonstrate the effectiveness of
UGG with state-of-the-art results in the recently released challenging Cast
Search in Movies and IARPA Janus Surveillance Video Benchmark dataset.

本文中，我们提出了一种名为 “Uncertainty-Gated Graph（UGG）” 的图形模型，用于在视频中进行人脸识别并在最近公开的具有挑战性的 Cast Search in Movies 和 IARPA Janus Surveillance Video Benchmark 数据集中达到最新的成果，UGG 可以通过环境连接进行身份传播，并通过适应性地更新边缘门的权重来显式地建模噪声上下文连接的不确定性。