This paper proposes a cross-modal distillation framework, PartDistill, which
transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D
shape part segmentation. PartDistill addresses three major challenges in this
task: the lack of 3D segmentation in invisible or undetected regions in the 2D
projections, inaccurate and inconsistent 2D predictions by VLMs, and the lack
of knowledge accumulation across different 3D shapes. PartDistill consists of a
teacher network that uses a VLM to make 2D predictions and a student network
that learns from the 2D predictions while extracting geometrical features from
multiple 3D shapes to carry out 3D part segmentation. A bi-directional
distillation, including forward and backward distillations, is carried out
within the framework, where the former forward distills the 2D predictions to
the student network, and the latter improves the quality of the 2D predictions,
which subsequently enhances the final 3D part segmentation. Moreover,
PartDistill can exploit generative models that facilitate effortless 3D shape
creation for generating knowledge sources to be distilled. Through extensive
experiments, PartDistill boosts the existing methods with substantial margins
on widely used ShapeNetPart and PartE datasets, by more than 15% and 12% higher
mIoU scores, respectively.

该研究提出了一个跨模态蒸馏框架 PartDistill，将二维视觉 - 语言模型的知识转移给三维形状的部分分割，通过解决三个主要挑战（二维投影中不可见或未检测区域的三维分割缺失、VLM 的二维预测不准确和不一致、不同三维形状之间的知识积累不足），PartDistill 在该任务中取得了显著的进展。通过在框架内进行正向和反向的蒸馏，PartDistill 能够提高最终的三维部分分割结果，并且可以利用生成模型生成用于蒸馏的知识源。在广泛使用的 ShapeNetPart 和 PartE 数据集上进行的大量实验证明，PartDistill 相比现有方法在 mIoU 分数上显著提升，分别高出 15% 和 12%。

PartDistill: 视觉语言模型蒸馏下的三维形状部分分割

PartDistill: 3D Shape Part Segmentation by Vision-Language Model  Distillation

By default, neural networks learn on all training data at once. When such a
model is trained on sequential chunks of new data, it tends to catastrophically
forget how to handle old data. In this work we investigate how continual
learners learn and forget representations. We observe two phenomena: knowledge
accumulation, i.e. the improvement of a representation over time, and feature
forgetting, i.e. the loss of task-specific representations. To better
understand both phenomena, we introduce a new analysis technique called task
exclusion comparison. If a model has seen a task and it has not forgotten all
the task-specific features, then its representation for that task should be
better than that of a model that was trained on similar tasks, but not that
exact one. Our image classification experiments show that most task-specific
features are quickly forgotten, in contrast to what has been suggested in the
past. Further, we demonstrate how some continual learning methods, like replay,
and ideas from representation learning affect a continually learned
representation. We conclude by observing that representation quality is tightly
correlated with continual learning performance.

本文研究神经网络在不断学习时如何学习和忘记其代表特征并引入了任务排除比较等新的分析技术。研究发现大部分特定任务的特征很快被遗忘，但回放和代表特征学习等方法可影响代表特征及其品质与学习绩效的紧密相关性。

不断学习的表示中的知识积累与特征遗忘问题

Knowledge Accumulation in Continually Learned Representations and the  Issue of Feature Forgetting

A major goal of artificial intelligence (AI) is to create an agent capable of
acquiring a general understanding of the world. Such an agent would require the
ability to continually accumulate and build upon its knowledge as it encounters
new experiences. Lifelong or continual learning addresses this setting, whereby
an agent faces a continual stream of problems and must strive to capture the
knowledge necessary for solving each new task it encounters. If the agent is
capable of accumulating knowledge in some form of compositional representation,
it could then selectively reuse and combine relevant pieces of knowledge to
construct novel solutions. Despite the intuitive appeal of this simple idea,
the literatures on lifelong learning and compositional learning have proceeded
largely separately. In an effort to promote developments that bridge between
the two fields, this article surveys their respective research landscapes and
discusses existing and future connections between them.

该研究论文探讨了终身学习和组合学习的研究现状，并讨论两个领域之间的联系，旨在培养能够获得世界一般理解的智能代理。

如何重复使用和组合知识以应对终身任务：持续学习和功能组合综述

How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition

Continual Learning (CL) on time series data represents a promising but
under-studied avenue for real-world applications. We propose two new CL
benchmarks for Human State Monitoring. We carefully designed the benchmarks to
mirror real-world environments in which new subjects are continuously added. We
conducted an empirical evaluation to assess the ability of popular CL
strategies to mitigate forgetting in our benchmarks. Our results show that,
possibly due to the domain-incremental properties of our benchmarks, forgetting
can be easily tackled even with a simple finetuning and that existing
strategies struggle in accumulating knowledge over a fixed, held-out, test
subject.

本文提出了两个与实际环境相似的 CL 基准，分别用于人类状态监测中，研究结果表明，由于基准具有增量属性，遗忘容易被解决，现有策略在固定测试对象上累积知识方面存在困难。