This paper addresses the unrealistic aspect of the commonly adopted
Continuous Incremental Semantic Segmentation (CISS) scenario, termed
overlapped. We point out that overlapped allows the same image to reappear in
future tasks with different pixel labels, which is far from practical
incremental learning scenarios. Moreover, we identified that this flawed
scenario may lead to biased results for two commonly used techniques in CISS,
pseudo-labeling and exemplar memory, resulting in unintended advantages or
disadvantages for certain techniques. To mitigate this, a practical scenario
called partitioned is proposed, in which the dataset is first divided into
distinct subsets representing each class, and then the subsets are assigned to
each corresponding task. This efficiently addresses the issue above while
meeting the requirement of CISS scenario, such as capturing the background
shifts. Furthermore, we identify and address the code implementation issues
related to retrieving data from the exemplar memory, which was ignored in
previous works. Lastly, we introduce a simple yet competitive memory-based
baseline, MiB-AugM, that handles background shifts of current tasks in the
exemplar memory. This baseline achieves state-of-the-art results across
multiple tasks involving learning numerous new classes.

本文针对常用的连续增量语义分割（Continuous Incremental Semantic Segmentation，CISS）场景中的不切实际之处进行了研究，提出了一个实用的场景，即分割（partitioned），该场景能够解决 CISS 中的偏差问题，并通过引入一种名为 MiB-AugM 的基于记忆机制的基准模型，实现了多任务学习与学习新类的最新结果。

面向实际的递增场景在类递增语义分割中的应用

Towards Realistic Incremental Scenario in Class Incremental Semantic  Segmentation

This paper introduces a solid state-of-the-art baseline for a
class-incremental semantic segmentation (CISS) problem. While the recent CISS
algorithms utilize variants of the knowledge distillation (KD) technique to
tackle the problem, they failed to fully address the critical challenges in
CISS causing the catastrophic forgetting; the semantic drift of the background
class and the multi-label prediction issue. To better address these challenges,
we propose a new method, dubbed SSUL-M (Semantic Segmentation with Unknown
Label with Memory), by carefully combining techniques tailored for semantic
segmentation. Specifically, we claim three main contributions. (1) defining
unknown classes within the background class to help to learn future classes
(help plasticity), (2) freezing backbone network and past classifiers with
binary cross-entropy loss and pseudo-labeling to overcome catastrophic
forgetting (help stability), and (3) utilizing tiny exemplar memory for the
first time in CISS to improve both plasticity and stability. The extensively
conducted experiments show the effectiveness of our method, achieving
significantly better performance than the recent state-of-the-art baselines on
the standard benchmark datasets. Furthermore, we justify our contributions with
thorough ablation analyses and discuss different natures of the CISS problem
compared to the traditional class-incremental learning targeting
classification. The official code is available at
this https URL

本文提出了一种新的方法 SSUL-M 方法，该方法结合了针对语义分割的技术并首次使用了微小样本存储器来解决类逐步学习中经常出现的语义漂移和多标签预测问题，实验证明其比其他最新技术基线表现更优秀。

SSUL: 基于示例的类增量学习的未知标签语义分割

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based  Class-Incremental Learning

This work considers the problem of unsupervised domain adaptation in person
re-identification (re-ID), which aims to transfer knowledge from the source
domain to the target domain. Existing methods are primary to reduce the
inter-domain shift between the domains, which however usually overlook the
relations among target samples. This paper investigates into the intra-domain
variations of the target domain and proposes a novel adaptation framework
w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance,
Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar
memory is introduced to store features of samples, which can effectively and
efficiently enforce the invariance constraints over the global dataset. We
further present the Graph-based Positive Prediction (GPP) method to explore
reliable neighbors for the target domain, which is built upon the memory and is
trained on the source samples. Experiments demonstrate that 1) the three
invariance properties are indispensable for effective domain adaptation, 2) the
memory plays a key role in implementing invariance learning and improves the
performance with limited extra computation cost, 3) GPP could facilitate the
invariance learning and thus significantly improves the results, and 4) our
approach produces new state-of-the-art adaptation accuracy on three re-ID
large-scale benchmarks.

本文研究了人物重识别中的无监督域自适应问题，提出了一种基于样例、相邻和摄像机属性的不变性学习框架，并在三个重识别基准测试中取得了新的最佳结果。