Existing deep trackers are typically trained with largescale video frames
with annotated bounding boxes. However, these bounding boxes are expensive and
time-consuming to annotate, in particular for large scale datasets. In this
paper, we propose to learn tracking representations from single point
annotations (i.e., 4.5x faster to annotate than the traditional bounding box)
in a weakly supervised manner. Specifically, we propose a soft contrastive
learning (SoCL) framework that incorporates target objectness prior into
end-to-end contrastive learning. Our SoCL consists of adaptive positive and
negative sample generation, which is memory-efficient and effective for
learning tracking representations. We apply the learned representation of SoCL
to visual tracking and show that our method can 1) achieve better performance
than the fully supervised baseline trained with box annotations under the same
annotation time cost; 2) achieve comparable performance of the fully supervised
baseline by using the same number of training frames and meanwhile reducing
annotation time cost by 78% and total fees by 85%; 3) be robust to annotation
noise.

通过单点注释学习跟踪表示，提出了一种软对比学习框架（SoCL），该框架将目标拥有性先验融合到端到端的对比学习中，并将所学表示应用于视觉跟踪，展示出优越的性能和鲁棒性。

从单点注释中学习追踪表示

Learning Tracking Representations from Single Point Annotations

Metaphor is a prominent linguistic device in human language and literature,
as they add color, imagery, and emphasis to enhance effective communication.
This paper introduces a large-scale high quality annotated Chinese Metaphor
Corpus, which comprises around 28K sentences drawn from a diverse range of
Chinese literary sources, such as poems, prose, song lyrics, etc. To ensure the
accuracy and consistency of our annotations, we introduce a comprehensive set
of guidelines. These guidelines address the facets of metaphor annotation,
including identifying tenors, vehicles, and grounds to handling the
complexities of similes, personifications, juxtapositions, and hyperboles.
Breaking tradition, our approach to metaphor generation emphasizes grounds and
their distinct features rather than the conventional combination of tenors and
vehicles. By integrating "ground" as a CoT (Chain of Thoughts) input, we are
able to generate metaphors that resonate more with real-world intuition. We
test generative models such as Belle, Baichuan, and Chinese-alpaca-33B using
our annotated corpus. These models are able to generate creative and fluent
metaphor sentences more frequently induced by selected samples from our
dataset, demonstrating the value of our corpus for Chinese metaphor research.
The code is available in the
this https URL

该研究介绍了一个大规模高质量的中文隐喻语料库，并提供了一套全面的准则作为注释指南，通过集成 “ground” 作为 CoT（思维链）输入，能够更多地产生与现实直觉产生共鸣的隐喻，测试了 Belie、Baichuan 和 Chinese-alpaca-33B 等生成模型，并展示了该语料库在中文隐喻研究中的价值。

CMDAG：一个带有标注基础的中文隐喻数据集，用于提升隐喻生成

CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for  Boosting Metaphor Generation

This paper introduces CODA-19, a human-annotated dataset that codes the
Background, Purpose, Method, Finding/Contribution, and Other sections of 10,966
English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by
248 crowd workers from Amazon Mechanical Turk within 10 days, and achieved
labeling quality comparable to that of experts. Each abstract was annotated by
nine different workers, and the final labels were acquired by majority vote.
The inter-annotator agreement (Cohen's kappa) between the crowd and the
biomedical expert (0.741) is comparable to inter-expert agreement (0.788).
CODA-19's labels have an accuracy of 82.2% when compared to the biomedical
expert's labels, while the accuracy between experts was 85.0%. Reliable human
annotations help scientists access and integrate the rapidly accelerating
coronavirus literature, and also serve as the battery of AI/NLP research, but
obtaining expert annotations can be slow. We demonstrated that a non-expert
crowd can be rapidly employed at scale to join the fight against COVID-19.

介绍了 CODA-19 数据集，该数据集是人工注释的，包括对英文摘要中的背景、目的、方法、发现 / 贡献和其他部分的编码。数据集由 Amazon Mechanical Turk 的 248 名众包工人 10 天内创建，并获得高效的标注质量。