Recent work in unsupervised multi-object segmentation shows impressive
results by predicting motion from a single image despite the inherent ambiguity
in predicting motion without the next image. On the other hand, the set of
possible motions for an image can be constrained to a low-dimensional space by
considering the scene structure and moving objects in it. We propose to model
pixel-wise geometry and object motion to remove ambiguity in reconstructing
flow from a single image. Specifically, we divide the image into coherently
moving regions and use depth to construct flow bases that best explain the
observed flow in each region. We achieve state-of-the-art results in
unsupervised multi-object segmentation on synthetic and real-world datasets by
modeling the scene structure and object motion. Our evaluation of the predicted
depth maps shows reliable performance in monocular depth estimation.

本文提出一种基于像素几何和物体运动模型的运动预测方法，通过将图像分为运动一致的区域并利用深度构建最佳匹配的流场基础来减少从单个图像重建流场的不确定性，并在场景结构和物体运动建模方面取得了最新的结果，同时对预测深度图的评估显示出可靠的单眼深度估计性能。

低维物体运动多目标发现

Multi-Object Discovery by Low-Dimensional Object Motion

In many practical fluid dynamics experiments, measuring variables such as
velocity and pressure is possible only at a limited number of sensor locations,
or for a few two-dimensional planes in the flow. However, knowledge of the full
fields is necessary to understand the dynamics of many flows. Deep learning
reconstruction of full flow fields from sparse measurements has recently
garnered significant research interest, as a way of overcoming this limitation.
This task is referred to as the flow reconstruction (FR) task. In the present
study, we propose a convolutional autoencoder based neural network model,
dubbed FR3D, which enables FR to be carried out for three-dimensional flows
around extruded 3D objects with arbitrary cross-sections. An innovative mapping
approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D
to generalize its performance to objects not encountered during training. We
conclusively demonstrate this generalization capability using a dataset
composed of 80 training and 20 testing geometries, all randomly generated. We
show that the FR3D model reconstructs pressure and velocity components with a
few percentage points of error. Additionally, using these predictions, we
accurately estimate the Q-criterion fields as well lift and drag forces on the
geometries.

提出了基于卷积自编码器的神经网络模型 FR3D，用于流体动力学中三维流场的重构，可推广到马鞍形的平面之外，能够以很少的误差重构出压力和速度组成，且能准确估计 Q-criterion 场以及提取几何物体的升力和阻力。

基于共形映射卷积自编码器的任意钝体非定常流三维重构和力估计

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Arbitrary Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders

In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that
integrates Flow reconstruction and Frame prediction seamlessly to handle Video
Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level
Memory modules in an Autoencoder with Skip Connections) to memorize normal
patterns for optical flow reconstruction so that abnormal events can be
sensitively identified with larger flow reconstruction errors. More
importantly, conditioned on the reconstructed flows, we then employ a
Conditional Variational Autoencoder (CVAE), which captures the high correlation
between video frame and optical flow, to predict the next frame given several
previous frames. By CVAE, the quality of flow reconstruction essentially
influences that of frame prediction. Therefore, poorly reconstructed optical
flows of abnormal events further deteriorate the quality of the final predicted
future frame, making the anomalies more detectable. Experimental results
demonstrate the effectiveness of the proposed method. Code is available at
\href{https://github.com/LiUzHiAn/hf2vad}{this https URL}.

本文提出了 $	ext {HF}^2$-VAD，这是一个混合框架，无缝地融合了流重建和帧预测，以处理视频异常检测。通过使用多级内存模块和跳过连接的自动编码器为正常光流模式进行记忆来识别异常事件，并且利用条件变分自动编码器，根据先前的几个帧预测下一个帧，根据预测的帧重定义优化后的光流，从而提高异常事件的检测能力。实验结果表明，所提出的方法的有效性。