The Argentinian real estate market presents a unique case study characterized
by its unstable and rapidly shifting macroeconomic circumstances over the past
decades. Despite the existence of a few datasets for price prediction, there is
a lack of mixed modality datasets specifically focused on Argentina. In this
paper, the first edition of ARED is introduced. A comprehensive real estate
price prediction dataset series, designed for the Argentinian market. This
edition contains information solely for Jan-Feb 2024. It was found that despite
the short time range captured by this zeroth edition (44 days), time dependent
phenomena has been occurring mostly on a market level (market as a whole).
Nevertheless future editions of this dataset, will most likely contain
historical data. Each listing in ARED comprises descriptive features, and
variable-length sets of images.

阐述了阿根廷房地产市场的特殊情况，介绍了一套设计用于阿根廷市场的全面房地产价格预测数据集系列，包括描述性特征和多种模态图像。

ARED：阿根廷房地产数据集

ARED: Argentina Real Estate Dataset

In the realm of research, the detection/recognition of text within
images/videos captured by cameras constitutes a highly challenging problem for
researchers. Despite certain advancements achieving high accuracy, current
methods still require substantial improvements to be applicable in practical
scenarios. Diverging from text detection in images/videos, this paper addresses
the issue of text detection within license plates by amalgamating multiple
frames of distinct perspectives. For each viewpoint, the proposed method
extracts descriptive features characterizing the text components of the license
plate, specifically corner points and area. Concretely, we present three
viewpoints: view-1, view-2, and view-3, to identify the nearest neighboring
components facilitating the restoration of text components from the same
license plate line based on estimations of similarity levels and distance
metrics. Subsequently, we employ the CnOCR method for text recognition within
license plates. Experimental results on the self-collected dataset
(PTITPlates), comprising pairs of images in various scenarios, and the publicly
available Stanford Cars Dataset, demonstrate the superiority of the proposed
method over existing approaches.

该篇论文提出了一种针对车牌内文本检测问题的方法，通过合并多个不同角度的图像帧，从车牌的角点和面积等特征中提取描述性特征，使用相似度估计和距离度量来恢复同一车牌行中的文本组件。随后，采用 CnOCR 方法进行车牌内文本识别。对自收集的数据集 (PTITPlates) 和公开的 Stanford Cars 数据集进行的实验结果表明，该方法在各种情景下都优于现有方法。

基于多角度视角模型的车牌识别

License Plate Recognition Based On Multi-Angle View Model

Vision-language models (VLMs) such as CLIP have shown promising performance
on a variety of recognition tasks using the standard zero-shot classification
procedure -- computing similarity between the query image and the embedded
words for each category. By only using the category name, they neglect to make
use of the rich context of additional information that language affords. The
procedure gives no intermediate understanding of why a category is chosen, and
furthermore provides no mechanism for adjusting the criteria used towards this
decision. We present an alternative framework for classification with VLMs,
which we call classification by description. We ask VLMs to check for
descriptive features rather than broad categories: to find a tiger, look for
its stripes; its claws; and more. By basing decisions on these descriptors, we
can provide additional cues that encourage using the features we want to be
used. In the process, we can get a clear idea of what features the model uses
to construct its decision; it gains some level of inherent explainability. We
query large language models (e.g., GPT-3) for these descriptors to obtain them
in a scalable way. Extensive experiments show our framework has numerous
advantages past interpretability. We show improvements in accuracy on ImageNet
across distribution shifts; demonstrate the ability to adapt VLMs to recognize
concepts unseen during training; and illustrate how descriptors can be edited
to effectively mitigate bias compared to the baseline.

本文介绍了一种基于描述符的视觉语言模型分类方法，通过查询大型语言模型获取描述符，实现从中获得更多的信息并提供可解释性；实验证明了该方法在图像分类精度，适应新概念和缓解偏差等方面有着广泛的优势。