While modern best practices advocate for scalable architectures that support
long-range interactions, object-centric models are yet to fully embrace these
architectures. In particular, existing object-centric models for handling
sequential inputs, due to their reliance on RNN-based implementation, show poor
stability and capacity and are slow to train on long sequences. We introduce
Parallelizable Spatiotemporal Binder or PSB, the first
temporally-parallelizable slot learning architecture for sequential inputs.
Unlike conventional RNN-based approaches, PSB produces object-centric
representations, known as slots, for all time-steps in parallel. This is
achieved by refining the initial slots across all time-steps through a fixed
number of layers equipped with causal attention. By capitalizing on the
parallelism induced by our architecture, the proposed model exhibits a
significant boost in efficiency. In experiments, we test PSB extensively as an
encoder within an auto-encoding framework paired with a wide variety of decoder
options. Compared to the state-of-the-art, our architecture demonstrates stable
training on longer sequences, achieves parallelization that results in a 60%
increase in training speed, and yields performance that is on par with or
better on unsupervised 2D and 3D object-centric scene decomposition and
understanding.

通过引入 Parallelizable Spatiotemporal Binder（PSB）作为第一个序列输入的时间可并行化槽学习架构，本文解决了现有基于 RNN 实现的面向对象模型在处理序列输入时的稳定性和容量问题，同时大大提高了训练效率，并在无监督 2D 和 3D 物体场景分解和理解方面取得了与或优于现有技术的性能。

并行时空绑定

Parallelized Spatiotemporal Binding

In recent years, deep neural networks have shown remarkable progress in dense
disparity estimation from dynamic scenes in monocular structured light systems.
However, their performance significantly drops when applied in unseen
environments. To address this issue, self-supervised online adaptation has been
proposed as a solution to bridge this performance gap. Unlike traditional
fine-tuning processes, online adaptation performs test-time optimization to
adapt networks to new domains. Therefore, achieving fast convergence during the
adaptation process is critical for attaining satisfactory accuracy. In this
paper, we propose an unsupervised loss function based on long sequential
inputs. It ensures better gradient directions and faster convergence. Our loss
function is designed using a multi-frame pattern flow, which comprises a set of
sparse trajectories of the projected pattern along the sequence. We estimate
the sparse pseudo ground truth with a confidence mask using a filter-based
method, which guides the online adaptation process. Our proposed framework
significantly improves the online adaptation speed and achieves superior
performance on unseen data.

深度神经网络在单目结构光系统中的密集视差估计方面取得了显著进展，但在未知环境中应用时性能显著下降。为了解决这个问题，提出了自监督在线适应作为解决方案来弥合这种性能差距。本文提出了一种基于长序列输入的无监督损失函数，确保更好的梯度方向和更快的收敛。我们的损失函数是使用多帧模式流设计的，该流由序列中的投影模式的一组稀疏轨迹组成。我们使用基于滤波器的方法估计稀疏伪地面真实性，它引导了在线适应过程。我们提出的框架显著提高了在线适应速度，并在未知数据上取得了卓越的性能。

结构光系统中用于动态场景的在线自适应视差估计

Online Adaptive Disparity Estimation for Dynamic Scenes in Structured  Light Systems

We demonstrate a simple strategy to cope with missing data in sequential
inputs, addressing the task of multilabel classification of diagnoses given
clinical time series. Collected from the pediatric intensive care unit (PICU)
at Children's Hospital Los Angeles, our data consists of multivariate time
series of observations. The measurements are irregularly spaced, leading to
missingness patterns in temporally discretized sequences. While these artifacts
are typically handled by imputation, we achieve superior predictive performance
by treating the artifacts as features. Unlike linear models, recurrent neural
networks can realize this improvement using only simple binary indicators of
missingness. For linear models, we show an alternative strategy to capture this
signal. Training models on missingness patterns only, we show that for some
diseases, what tests are run can be as predictive as the results themselves.

本文采用简单的策略解决临床时间序列中缺失数据的问题，通过将缺失数据视为特征，并利用简单的二进制指示器处理，实现了儿科重症监护病房（PICU）临床时间序列的多标签诊断分类预测。同时，我们也展示了基于缺失数据模式的训练模型不仅能刻画缺失数据信号，同时显示该信号的预测性可以超过部分疾病的测试结果本身。