In this paper, we tackle the problem of learning visual representations from
unlabeled scene-centric data. Existing works have demonstrated the potential of
utilizing the underlying complex structure within scene-centric data; still,
they commonly rely on hand-crafted objectness priors or specialized pretext
tasks to build a learning framework, which may harm generalizability. Instead,
we propose contrastive learning from data-driven semantic slots, namely
SlotCon, for joint semantic grouping and representation learning. The semantic
grouping is performed by assigning pixels to a set of learnable prototypes,
which can adapt to each sample by attentive pooling over the feature and form
new slots. Based on the learned data-dependent slots, a contrastive objective
is employed for representation learning, which enhances the discriminability of
features, and conversely facilitates grouping semantically coherent pixels
together. Compared with previous efforts, by simultaneously optimizing the two
coupled objectives of semantic grouping and contrastive learning, our approach
bypasses the disadvantages of hand-crafted priors and is able to learn
object/group-level representations from scene-centric images. Experiments show
our approach effectively decomposes complex scenes into semantic groups for
feature learning and significantly benefits downstream tasks, including object
detection, instance segmentation, and semantic segmentation. Code is available
at: this https URL

本文探讨了从无标签场景中心数据中学习视觉表示的问题，提出了一种的基于对比学习和数据驱动语义槽的语义聚类和表示学习方法，通过此方法能够更好的区分特征和语义相符的像素点，以此来有效地分解场景中的像素群组，并显著提高目标检测、实例分割和语义分割等下游应用的效果。

利用语义分组进行自监督视觉表示学习

Self-Supervised Visual Representation Learning with Semantic Grouping

The widespread use of conversational and question answering systems made it
necessary to improve the performances of speaker intent detection and
understanding of related semantic slots, i.e., Spoken Language Understanding
(SLU). Often, these tasks are approached with supervised learning methods,
which needs considerable labeled datasets. This paper presents the first
Italian dataset for SLU. It is derived through a semi-automatic procedure and
is used as a benchmark of various open source and commercial systems.

本研究介绍了第一个用于意图检测和语义槽理解的意大利数据集，并使用半自动化过程来建立该数据集，用于各种开源和商业系统的性能基准测试。

Almawave-SLU：意大利语 SLU 的新数据集

Almawave-SLU: A new dataset for SLU in Italian

Natural language understanding (NLU) is a core component of a spoken dialogue
system. Recently recurrent neural networks (RNN) obtained strong results on NLU
due to their superior ability of preserving sequential information over time.
Traditionally, the NLU module tags semantic slots for utterances considering
their flat structures, as the underlying RNN structure is a linear chain.
However, natural language exhibits linguistic properties that provide rich,
structured information for better understanding. This paper introduces a novel
model, knowledge-guided structural attention networks (K-SAN), a generalization
of RNN to additionally incorporate non-flat network topologies guided by prior
knowledge. There are two characteristics: 1) important substructures can be
captured from small training data, allowing the model to generalize to
previously unseen test data; 2) the model automatically figures out the salient
substructures that are essential to predict the semantic tags of the given
sentences, so that the understanding performance can be improved. The
experiments on the benchmark Air Travel Information System (ATIS) data show
that the proposed K-SAN architecture can effectively extract salient knowledge
from substructures with an attention mechanism, and outperform the performance
of the state-of-the-art neural network based frameworks.

本文介绍了一种知识引导的结构注意力网络 (K-SAN) 模型，其是循环神经网络 (RNN) 的推广，此模型可以自动识别关键的子结构并加以利用，以提高对于自然语言理解的性能，表现优于基于神经网络的现有框架。