Segmenting object parts such as cup handles and animal bodies is important in
many real-world applications but requires more annotation effort. The largest
dataset nowadays contains merely two hundred object categories, implying the
difficulty to scale up part segmentation to an unconstrained setting. To
address this, we propose to explore a seemingly simplified but empirically
useful and scalable task, class-agnostic part segmentation. In this problem, we
disregard the part class labels in training and instead treat all of them as a
single part class. We argue and demonstrate that models trained without part
classes can better localize parts and segment them on objects unseen in
training. We then present two further improvements. First, we propose to make
the model object-aware, leveraging the fact that parts are "compositions",
whose extents are bounded by the corresponding objects and whose appearances
are by nature not independent but bundled. Second, we introduce a novel
approach to improve part segmentation on unseen objects, inspired by an
interesting finding -- for unseen objects, the pixel-wise features extracted by
the model often reveal high-quality part segments. To this end, we propose a
novel self-supervised procedure that iterates between pixel clustering and
supervised contrastive learning that pulls pixels closer or pushes them away.
Via extensive experiments on PartImageNet and Pascal-Part, we show notable and
consistent gains by our approach, essentially a critical step towards
open-world part segmentation.

本文提出了一种利用无类别部件分割来减少标注难度并能适用于未知目标的部件分割方法，并通过对象感知性和自监督学习的方式对此进行了改进。

迈向基于开放世界的零件分割

Towards Open-World Segmentation of Parts

We present a new approach to modeling visual attributes. Prior work casts
attributes in a similar role as objects, learning a latent representation where
properties (e.g., sliced) are recognized by classifiers much in the way objects
(e.g., apple) are. However, this common approach fails to separate the
attributes observed during training from the objects with which they are
composed, making it ineffectual when encountering new attribute-object
compositions. Instead, we propose to model attributes as operators. Our
approach learns a semantic embedding that explicitly factors out attributes
from their accompanying objects, and also benefits from novel regularizers
expressing attribute operators' effects (e.g., blunt should undo the effects of
sharp). Not only does our approach align conceptually with the linguistic role
of attributes as modifiers, but it also generalizes to recognize unseen
compositions of objects and attributes. We validate our approach on two
challenging datasets and demonstrate significant improvements over the
state-of-the-art. In addition, we show that not only can our model recognize
unseen compositions robustly in an open-world setting, it can also generalize
to compositions where objects themselves were unseen during training.

我们提出了一种新的视觉属性建模方法，将属性视为操作符。该方法使用语义嵌入明确地将属性与它们伴随的对象分离，并且具有表达属性操作符效果的新型正则化机制，从而实现了对对象和属性的新组合的泛化。我们在两个具有挑战性的数据集上验证了我们的方法，并证明了其相对于最先进方法的显著改进。

属性作为操作符：分解未见过的属性 - 对象组合

Attributes as Operators: Factorizing Unseen Attribute-Object  Compositions

Our goal is to create a convenient natural language interface for performing
well-specified but complex actions such as analyzing data, manipulating text,
and querying databases. However, existing natural language interfaces for such
tasks are quite primitive compared to the power one wields with a programming
language. To bridge this gap, we start with a core programming language and
allow users to "naturalize" the core language incrementally by defining
alternative, more natural syntax and increasingly complex concepts in terms of
compositions of simpler ones. In a voxel world, we show that a community of
users can simultaneously teach a common system a diverse language and use it to
build hundreds of complex voxel structures. Over the course of three days,
these users went from using only the core language to using the naturalized
language in 85.9\% of the last 10K utterances.

本文讲述如何通过核心编程语言并允许用户自然地增量定义替代语法和越来越复杂的概念来创建方便的自然语言接口，以在类似于分析数据、操作文本和查询数据库等复杂的任务中实现与编程语言同等的能力。作者在一个体素世界中展示了一个用户社区可以同时教授一个共同系统各种不同的语言，并用它来构建数百个复杂的体素结构的例子。三天内，这些用户从只使用核心语言到 85.9％的后 10K 个语句中使用自然化语言。