We are interested in aligning how people think about objects and what
machines perceive, meaning by this the fact that object recognition, as
performed by a machine, should follow a process which resembles that followed
by humans when thinking of an object associated with a certain concept. The
ultimate goal is to build systems which can meaningfully interact with their
users, describing what they perceive in the users' own terms. As from the field
of Lexical Semantics, humans organize the meaning of words in hierarchies where
the meaning of, e.g., a noun, is defined in terms of the meaning of a more
general noun, its genus, and of one or more differentiating properties, its
differentia. The main tenet of this paper is that object recognition should
implement a hierarchical process which follows the hierarchical semantic
structure used to define the meaning of words. We achieve this goal by
implementing an algorithm which, for any object, recursively recognizes its
visual genus and its visual differentia. In other words, the recognition of an
object is decomposed in a sequence of steps where the locally relevant visual
features are recognized. This paper presents the algorithm and a first
evaluation.

本文提出了一种算法，通过递归识别物体的视觉属于和视觉特征来实现物体识别，从而实现了基于词义层次结构的物体识别，进一步推动了人机交互的研究。

自我中心的分层视觉语义

Egocentric Hierarchical Visual Semantics

Human language understanding operates at multiple levels of granularity
(e.g., words, phrases, and sentences) with increasing levels of abstraction
that can be hierarchically combined. However, existing deep models with stacked
layers do not explicitly model any sort of hierarchical process. This paper
proposes a recursive Transformer model based on differentiable CKY style binary
trees to emulate the composition process. We extend the bidirectional language
model pre-training objective to this architecture, attempting to predict each
word given its left and right abstraction nodes. To scale up our approach, we
also introduce an efficient pruned tree induction algorithm to enable encoding
in just a linear number of composition steps. Experimental results on language
modeling and unsupervised parsing show the effectiveness of our approach.

本文提出了一种基于递归 Transformer 模型的分层过程的方法，并将双向语言模型预训练目标扩展到此体系结构，试图预测每个单词给定其左右抽象节点。实验结果表明，我们的方法在语言建模和无监督解析方面非常有效。