Video processing is generally divided into two main categories: processing of
the entire video, which typically yields optimal classification outcomes, and
real-time processing, where the objective is to make a decision as promptly as
possible. The latter is often driven by the need to identify rapidly potential
critical or dangerous situations. These could include machine failure, traffic
accidents, heart problems, or dangerous behavior. Although the models dedicated
to the processing of entire videos are typically well-defined and clearly
presented in the literature, this is not the case for online processing, where
a plethora of hand-devised methods exist. To address this, we present \our{}, a
novel, unified, and theoretically-based adaptation framework for dealing with
the online classification problem for video data. The initial phase of our
study is to establish a robust mathematical foundation for the theory of
classification of sequential data, with the potential to make a decision at an
early stage. This allows us to construct a natural function that encourages the
model to return an outcome much faster. The subsequent phase is to demonstrate
a straightforward and readily implementable method for adapting offline models
to online and recurrent operations. Finally, by comparing the proposed approach
to the non-online state-of-the-art baseline, it is demonstrated that the use of
\our{} encourages the network to make earlier classification decisions without
compromising accuracy.

我们提出了一个适应在线分类问题的新颖、统一和基于理论的自适应框架，该框架旨在通过建立强大的数学基础来处理序列数据的分类，并在早期阶段返回结果，同时不影响准确性。

PrAViC：实时视频分类的概率适应框架

PrAViC: Probabilistic Adaptation Framework for Real-Time Video  Classification

Combinatorial optimization (CO) on graphs is a classic topic that has been
extensively studied across many scientific and industrial fields. Recently,
solving CO problems on graphs through learning methods has attracted great
attention. Advanced deep learning methods, e.g., graph neural networks (GNNs),
have been used to effectively assist the process of solving COs. However,
current frameworks based on GNNs are mainly designed for certain CO problems,
thereby failing to consider their transferable and generalizable abilities
among different COs on graphs. Moreover, simply using original graphs to model
COs only captures the direct correlations among objects, which does not
consider the mathematical logicality and properties of COs. In this paper, we
propose a unified pre-training and adaptation framework for COs on graphs with
the help of the maximum satisfiability (Max-SAT) problem. We first use Max-SAT
to bridge different COs on graphs since they can be converted to Max-SAT
problems represented by standard formulas and clauses with logical information.
Then, we further design a pre-training and domain adaptation framework to
extract the transferable and generalizable features so that different COs can
benefit from them. In the pre-training stage, Max-SAT instances are generated
to initialize the parameters of the model. In the fine-tuning stage, instances
from CO and Max-SAT problems are used for adaptation so that the transferable
ability can be further improved. Numerical experiments on several datasets show
that features extracted by our framework exhibit superior transferability and
Max-SAT can boost the ability to solve COs on graphs.

通过最大可满足性问题（Max-SAT）提出了一种统一的图上组合优化问题（CO）的预训练和适应框架，能够提取可转移和可推广的特征，以解决图上不同 CO 问题。

图上组合优化的统一预训练和适应框架

A Unified Pre-training and Adaptation Framework for Combinatorial  Optimization on Graphs

Recent works have proven that many relevant visual tasks are closely related
one to another. Yet, this connection is seldom deployed in practice due to the
lack of practical methodologies to transfer learned concepts across different
training processes. In this work, we introduce a novel adaptation framework
that can operate across both task and domains. Our framework learns to transfer
knowledge across tasks in a fully supervised domain (e.g., synthetic data) and
use this knowledge on a different domain where we have only partial supervision
(e.g., real data). Our proposal is complementary to existing domain adaptation
techniques and extends them to cross tasks scenarios providing additional
performance gains. We prove the effectiveness of our framework across two
challenging tasks (i.e., monocular depth estimation and semantic segmentation)
and four different domains (Synthia, Carla, Kitti, and Cityscapes).

本文提出了一种新的适应性框架，用于跨任务和领域传递知识，通过在一个完全监督域中学习跨任务和领域的知识，使其在只有部分监督域中实现性能增益。实验证明了该框架在两个具有挑战性的任务（即单眼深度估计和语义分割）和四个不同领域（Synthia、Carla、Kitti 和 Cityscapes）中的有效性。