Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past.
CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to
evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from
MSMT17 but due to limited number of unique persons, the diversity is limited.
On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively
shorter text description on average. Further, these datasets are captured in a
restricted environment with limited number of cameras. In order to further
diversify the identities and provide dense captions, we propose a novel dataset
called IIITD-20K. IIITD-20K comprises of 20,000 unique identities captured in
the wild and provides a rich dataset for text-to-image ReID. With a minimum of
26 words for a description, each image is densely captioned. We further
synthetically generate images and fine-grained captions using Stable-diffusion
and BLIP models trained on our dataset. We perform elaborate experiments using
state-of-art text-to-image ReID models and vision-language pre-trained models
and present a comprehensive analysis of the dataset. Our experiments also
reveal that synthetically generated data leads to a substantial performance
improvement in both same dataset as well as cross dataset settings. Our dataset
is available at this https URL

提出了一个新的名为 IIITD-20K 的数据集，包括 20000 个在野外抓取的唯一身份的密集标题，使用生成图像和精细的标题进一步多样化身份，并进行了实验以将其与目前最先进的文本到图像 ReID 模型进行对比。

IIITD-20K: 文本 - 图像 ReID 的密集字幕

IIITD-20K: Dense captioning for Text-Image ReID

Code-Mixed text data consists of sentences having words or phrases from more
than one language. Most multi-lingual communities worldwide communicate using
multiple languages, with English usually one of them. Hinglish is a Code-Mixed
text composed of Hindi and English but written in Roman script. This paper aims
to determine the factors influencing the quality of Code-Mixed text data
generated by the system. For the HinglishEval task, the proposed model uses
multi-lingual BERT to find the similarity between synthetically generated and
human-generated sentences to predict the quality of synthetically generated
Hinglish sentences.

本研究使用多语言 BERT 模型，通过比较合成和人工生成的句子之间的相似度，预测合成的 Hinglish 句子的质量，并确定影响系统生成 Code-Mixed 文本数据质量的因素。

使用 Transformer 对混合印地语文本进行质量评估的 HinglishEval：比尔积尼学院

BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers

This paper focuses on modeling ride requests and their variations over
location and time, based on analyzing extensive real-world data from a
ride-sharing service. We introduce a graph model that captures the spatial and
temporal variability of ride requests and the potentials for ride pooling. We
discover these ride request graphs exhibit a well known property called
densification power law often found in real graphs modelling human behaviors.
We show the pattern of ride requests and the potential of ride pooling for a
city can be characterized by the densification factor of the ride request
graphs. Previous works have shown that it is possible to automatically generate
synthetic versions of these graphs that exhibit a given densification factor.
We present an algorithm for automatic generation of synthetic ride request
graphs that match quite well the densification factor of ride request graphs
from actual ride request data.

本论文基于详细分析真实世界来自共享乘车服务的数据，专注于对乘车请求和其时空变化进行建模。作者提出了一个图形模型来捕捉乘车请求的时空变异性和共乘的潜力，并发现这些乘车请求图形展现了人类行为建模的常见特征 - 稠密化功率法则。通过使用自动合成算法，本研究提供了一种自动生成满足实际数据的图形的方法。