Open Domain Generalization (ODG) is a challenging task as it not only deals
with distribution shifts but also category shifts between the source and target
datasets. To handle this task, the model has to learn a generalizable
representation that can be applied to unseen domains while also identify
unknown classes that were not present during training. Previous work has used
multiple source-specific networks, which involve a high computation cost.
Therefore, this paper proposes a method that can handle ODG using only a single
network. The proposed method utilizes a head that is pre-trained by
linear-probing and employs two regularization terms, each targeting the
regularization of feature extractor and the classification head, respectively.
The two regularization terms fully utilize the pre-trained features and
collaborate to modify the head of the model without excessively altering the
feature extractor. This ensures a smoother softmax output and prevents the
model from being biased towards the source domains. The proposed method shows
improved adaptability to unseen domains and increased capability to detect
unseen classes as well. Extensive experiments show that our method achieves
competitive performance in several benchmarks. We also justify our method with
careful analysis of the effect on the logits, features, and the head.

本文针对开放领域泛化问题，通过使用单个网络以及预训练的头部和两个正则化项进行特征提取、分类和模型修改，使模型适应未知领域并提高对未知类别的检测能力，最终在多个基准测试中取得了有竞争力的性能。

利用预训练特征通过正则化来进行单一网络的开放域泛化

Open Domain Generalization with a Single Network by Regularization  Exploiting Pre-trained Features

We present a unified method, termed Unicorn, that can simultaneously solve
four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the
same model parameters. Due to the fragmented definitions of the object tracking
problem itself, most existing trackers are developed to address a single or
part of tasks and overspecialize on the characteristics of specific tasks. By
contrast, Unicorn provides a unified solution, adopting the same input,
backbone, embedding, and head across all tracking tasks. For the first time, we
accomplish the great unification of the tracking network architecture and
learning paradigm. Unicorn performs on-par or better than its task-specific
counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17,
BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. We believe that Unicorn will
serve as a solid step towards the general vision model. Code is available at
this https URL

提出了一种统一方法 Unicorn，可以通过单个网络和相同的模型参数同时解决四个跟踪问题 (SOT、MOT、VOS 和 MOTS)，该方法采用相同的输入、主干、嵌入和头部，解决了现有跟踪器过度专业化特定任务的问题，并在包括 LaSOT、TrackingNet、MOT17、BDD100K、DAVIS16-17、MOTS20 和 BDD100K MOTS 在内的 8 个跟踪数据集上实现了与其特定任务的对应产品相当或更好的表现。

物体追踪的大一统

Towards Grand Unification of Object Tracking

Current popular online multi-object tracking (MOT) solutions apply single
object trackers (SOTs) to capture object motions, while often requiring an
extra affinity network to associate objects, especially for the occluded ones.
This brings extra computational overhead due to repetitive feature extraction
for SOT and affinity computation. Meanwhile, the model size of the
sophisticated affinity network is usually non-trivial. In this paper, we
propose a novel MOT framework that unifies object motion and affinity model
into a single network, named UMA, in order to learn a compact feature that is
discriminative for both object motion and affinity measure. In particular, UMA
integrates single object tracking and metric learning into a unified triplet
network by means of multi-task learning. Such design brings advantages of
improved computation efficiency, low memory requirement and simplified training
procedure. In addition, we equip our model with a task-specific attention
module, which is used to boost task-aware feature learning. The proposed UMA
can be easily trained end-to-end, and is elegant - requiring only one training
stage. Experimental results show that it achieves promising performance on
several MOT Challenge benchmarks.

本论文提出了一种新的 MOT 框架，即 UMA，该框架将对象运动和亲和模型统一到一个网络中，通过多任务学习，将单目标跟踪和度量学习集成在一个统一的三元组网络中。实验结果表明，UMA 在几个 MOT 挑战基准上取得了有希望的性能。