Domain adaptation for object detection typically entails transferring
knowledge from one visible domain to another visible domain. However, there are
limited studies on adapting from the visible to the thermal domain, because the
domain gap between the visible and thermal domains is much larger than
expected, and traditional domain adaptation can not successfully facilitate
learning in this situation. To overcome this challenge, we propose a
Distinctive Dual-Domain Teacher (D3T) framework that employs distinct training
paradigms for each domain. Specifically, we segregate the source and target
training sets for building dual-teachers and successively deploy exponential
moving average to the student model to individual teachers of each domain. The
framework further incorporates a zigzag learning method between dual teachers,
facilitating a gradual transition from the visible to thermal domains during
training. We validate the superiority of our method through newly designed
experimental protocols with well-known thermal datasets, i.e., FLIR and KAIST.
Source code is available at this https URL .

通过 Distinctive Dual-Domain Teacher (D3T) 框架和新设计的实验方案，验证了在从可见到热成像域进行领域自适应时，我们方法的优越性。

D3T: 通过 RGB - 红外差异双域教师在领域自适应目标检测中穿梭

D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap  for Domain-Adaptive Object Detection

Despite continual learning's long and well-established academic history, its
application in real-world scenarios remains rather limited. This paper contends
that this gap is attributable to a misalignment between the actual challenges
of continual learning and the evaluation protocols in use, rendering proposed
solutions ineffective for addressing the complexities of real-world setups. We
validate our hypothesis and assess progress to date, using a new 3D semantic
segmentation benchmark, OCL-3DSS. We investigate various continual learning
schemes from the literature by utilizing more realistic protocols that
necessitate online and continual learning for dynamic, real-world scenarios
(eg., in robotics and 3D vision applications). The outcomes are sobering: all
considered methods perform poorly, significantly deviating from the upper bound
of joint offline training. This raises questions about the applicability of
existing methods in realistic settings. Our paper aims to initiate a paradigm
shift, advocating for the adoption of continual learning methods through new
experimental protocols that better emulate real-world conditions to facilitate
breakthroughs in the field.

通过使用模拟真实世界条件的新实验协议，本文验证了关于连续学习的假设，并评估迄今取得的进展。结果表明，考虑到所有方法均表现不佳，明显偏离联合离线训练的上限，这对现实环境中的现有方法的适用性提出了问题。本文旨在通过新的实验协议来倡导采用连续学习方法，以在该领域取得突破。

持续学习是否为现实世界的挑战做好准备？

Is Continual Learning Ready for Real-world Challenges?

Humans possess an innate ability to identify and differentiate instances that
they are not familiar with, by leveraging and adapting the knowledge that they
have acquired so far. Importantly, they achieve this without deteriorating the
performance on their earlier learning. Inspired by this, we identify and
formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery
without Forgetting, which tasks a machine learning model to incrementally
discover novel categories of instances from unlabeled data, while maintaining
its performance on the previously seen categories. We propose 1) a method to
generate pseudo-latent representations which act as a proxy for (no longer
available) labeled data, thereby alleviating forgetting, 2) a
mutual-information based regularizer which enhances unsupervised discovery of
novel classes, and 3) a simple Known Class Identifier which aids generalized
inference when the testing data contains instances form both seen and unseen
categories. We introduce experimental protocols based on CIFAR-10, CIFAR-100
and ImageNet-1000 to measure the trade-off between knowledge retention and
novel class discovery. Our extensive evaluations reveal that existing models
catastrophically forget previously seen categories while identifying novel
categories, while our method is able to effectively balance between the
competing objectives. We hope our work will attract further research into this
newly identified pragmatic problem setting.

本文提出了 NCDwF (无遗忘新类别发现) 这一新的实际问题，并使用生成拟变量表示、基于互信息的正则化以及已知类别标识符等方法进行了研究，通过基于 CIFAR-10、CIFAR-100 和 ImageNet-1000 的实验验证了其在已有知识保留和新类别发现之间的平衡效果。

无遗忘新类别发现

Novel Class Discovery without Forgetting

Zero-Shot Action Recognition has attracted attention in the last years and
many approaches have been proposed for recognition of objects, events and
actions in images and videos. There is a demand for methods that can classify
instances from classes that are not present in the training of models,
especially in the complex problem of automatic video understanding, since
collecting, annotating and labeling videos are difficult and laborious tasks.
We have identified that there are many methods available in the literature,
however, it is difficult to categorize which techniques can be considered state
of the art. Despite the existence of some surveys about zero-shot action
recognition in still images and experimental protocol, there is no work focused
on videos. Therefore, we present a survey of the methods that comprise
techniques to perform visual feature extraction and semantic feature extraction
as well to learn the mapping between these features considering specifically
zero-shot action recognition in videos. We also provide a complete description
of datasets, experiments and protocols, presenting open issues and directions
for future work, essential for the development of the computer vision research
field.

本论文介绍了零样本动作识别在视频方面的研究现状，总结了可视化特征提取和语义特征提取的方法，并提供了完整的数据集、实验和协议描述，指出了未来工作的方向。