Event-based object detection has recently garnered attention in the computer
vision community due to the exceptional properties of event cameras, such as
high dynamic range and no motion blur. However, feature asynchronism and
sparsity cause invisible objects due to no relative motion to the camera,
posing a significant challenge in the task. Prior works have studied various
memory mechanisms to preserve as many features as possible at the current time,
guided by temporal clues. While these implicit-learned memories retain some
short-term information, they still struggle to preserve long-term features
effectively. In this paper, we consider those invisible objects as
pseudo-occluded objects and aim to reveal their features. Firstly, we introduce
visibility attribute of objects and contribute an auto-labeling algorithm to
append additional visibility labels on an existing event camera dataset.
Secondly, we exploit tracking strategies for pseudo-occluded objects to
maintain their permanence and retain their bounding boxes, even when features
have not been available for a very long time. These strategies can be treated
as an explicit-learned memory guided by the tracking objective to record the
displacements of objects across frames. Lastly, we propose a spatio-temporal
feature aggregation module to enrich the latent features and a consistency loss
to increase the robustness of the overall pipeline. We conduct comprehensive
experiments to verify our method's effectiveness where still objects are
retained but real occluded objects are discarded. The results demonstrate that
(1) the additional visibility labels can assist in supervised training, and (2)
our method outperforms state-of-the-art approaches with a significant
improvement of 7.9% absolute mAP.

在这篇论文中，我们考虑将不可见的物体视为伪遮挡物体，并旨在揭示它们的特征。我们提出了可视属性和自动标注算法来处理现有事件相机数据集，并利用跟踪策略来保持伪遮挡物体的永久性和边界框，同时引入了时空特征聚合模块和一致性损失以提高整体流程的稳健性。实验证明，额外的可见性标签可以辅助有监督训练，并且我们的方法在绝对 mAP 上比最先进的方法表现出显著的改进，提高了 7.9%。

基于轨迹辅助的事件摄像头物体检测

Tracking-Assisted Object Detection with Event Cameras

Recovering temporally consistent 3D human body pose, shape and motion from a
monocular video is a challenging task due to (self-)occlusions, poor lighting
conditions, complex articulated body poses, depth ambiguity, and limited
availability of annotated data. Further, doing a simple perframe estimation is
insufficient as it leads to jittery and implausible results. In this paper, we
propose a novel method for temporally consistent motion estimation from a
monocular video. Instead of using generic ResNet-like features, our method uses
a body-aware feature representation and an independent per-frame pose and
camera initialization over a temporal window followed by a novel
spatio-temporal feature aggregation by using a combination of self-similarity
and self-attention over the body-aware features and the perframe
initialization. Together, they yield enhanced spatiotemporal context for every
frame by considering remaining past and future frames. These features are used
to predict the pose and shape parameters of the human body model, which are
further refined using an LSTM. Experimental results on the publicly available
benchmark data show that our method attains significantly lower acceleration
error and outperforms the existing state-of-the-art methods over all key
quantitative evaluation metrics, including complex scenarios like partial
occlusion, complex poses and even relatively low illumination.

提出了一种从单眼视频中恢复时间一致的 3D 人体姿势、形状和动作的方法，通过使用具有身体感知特征表示和独立的逐帧姿势和相机初始化，结合自相似性和自注意力等技术进行时空特征聚合，该方法提供了增强的时空上下文，可在各种复杂场景下得到更准确的姿势估计和形状重建。

增强时空上下文：单目视频中具有时态一致性的稳健 3D 人体动作恢复

Enhanced Spatio-Temporal Context for Temporally Consistent Robust 3D  Human Motion Recovery from Monocular Videos

Despite the significant progress made by deep learning in natural image
matting, there has been so far no representative work on deep learning for
video matting due to the inherent technical challenges in reasoning temporal
domain and lack of large-scale video matting datasets. In this paper, we
propose a deep learning-based video matting framework which employs a novel and
effective spatio-temporal feature aggregation module (ST-FAM). As optical flow
estimation can be very unreliable within matting regions, ST-FAM is designed to
effectively align and aggregate information across different spatial scales and
temporal frames within the network decoder. To eliminate frame-by-frame trimap
annotations, a lightweight interactive trimap propagation network is also
introduced. The other contribution consists of a large-scale video matting
dataset with groundtruth alpha mattes for quantitative evaluation and
real-world high-resolution videos with trimaps for qualitative evaluation.
Quantitative and qualitative experimental results show that our framework
significantly outperforms conventional video matting and deep image matting
methods applied to video in presence of multi-frame temporal information.

本文介绍了一个深度学习的视频抠图框架，使用了一种新颖和有效的时空特征聚合模块 (ST-FAM)，同时引入了一种轻量级的交互式 trimap 传播网络，配合一个大规模的视频抠图数据集进行评估，实验结果表明，该框架在具有多帧时间信息的视频中，在定量和定性实验中均显著优于传统视频抠图和应用于视频的深度图像抠图方法。