Novel dataset creation for all multi-object tracking, crowd-counting, and
industrial-based videos is arduous and time-consuming when faced with a unique
class that densely populates a video sequence. We propose a time efficient
method called POPCat that exploits the multi-target and temporal features of
video data to produce a semi-supervised pipeline for segmentation or box-based
video annotation. The method retains the accuracy level associated with human
level annotation while generating a large volume of semi-supervised annotations
for greater generalization. The method capitalizes on temporal features through
the use of a particle tracker to expand the domain of human-provided target
points. This is done through the use of a particle tracker to reassociate the
initial points to a set of images that follow the labeled frame. A YOLO model
is then trained with this generated data, and then rapidly infers on the target
video. Evaluations are conducted on GMOT-40, AnimalTrack, and Visdrone-2019
benchmarks. These multi-target video tracking/detection sets contain multiple
similar-looking targets, camera movements, and other features that would
commonly be seen in "wild" situations. We specifically choose these difficult
datasets to demonstrate the efficacy of the pipeline and for comparison
purposes. The method applied on GMOT-40, AnimalTrack, and Visdrone shows a
margin of improvement on recall/mAP50/mAP over the best results by a value of
24.5%/9.6%/4.8%, -/43.1%/27.8%, and 7.5%/9.4%/7.5% where metrics were
collected.

通过利用视频数据的多目标和时间特征，我们提出了一种名为 POPCat 的高效方法来创建用于分割或基于框的视频注释的半监督管道，使其在产生更大泛化能力的半监督注释的同时保持与人工注释相当的准确性。该方法通过使用粒子追踪器利用时间特征来扩展人工提供的目标点的域，然后利用此生成的数据训练 YOLO 模型，并快速推断目标视频。对 GMOT-40、AnimalTrack 和 Visdrone-2019 基准进行评估，这些多目标视频跟踪 / 检测集包含多个外观相似的目标、摄像机运动和其他在 “野外” 情况中常见的特征。我们特别选择这些困难的数据集来展示该管道的有效性并进行比较，结果显示在 GMOT-40、AnimalTrack 和 Visdrone 上相对于最佳结果的召回率 /mAP50/mAP 上分别提高了 24.5%/9.6%/4.8%、-/43.1%/27.8% 和 7.5%/9.4%/7.5%。

POPCat: 复杂注释任务的粒子传播

POPCat: Propagation of particles for complex annotation tasks

Extracting key information from scientific papers has the potential to help
researchers work more efficiently and accelerate the pace of scientific
progress. Over the last few years, research on Scientific Information
Extraction (SciIE) witnessed the release of several new systems and benchmarks.
However, existing paper-focused datasets mostly focus only on specific parts of
a manuscript (e.g., abstracts) and are single-modality (i.e., text- or
table-only), due to complex processing and expensive annotations. Moreover,
core information can be present in either text or tables or across both. To
close this gap in data availability and enable cross-modality IE, while
alleviating labeling costs, we propose a semi-supervised pipeline for
annotating entities in text, as well as entities and relations in tables, in an
iterative procedure. Based on this pipeline, we release novel resources for the
scientific community, including a high-quality benchmark, a large-scale corpus,
and a semi-supervised annotation pipeline. We further report the performance of
state-of-the-art IE models on the proposed benchmark dataset, as a baseline.
Lastly, we explore the potential capability of large language models such as
ChatGPT for the current task. Our new dataset, results, and analysis validate
the effectiveness and efficiency of our semi-supervised pipeline, and we
discuss its remaining limitations.

从科学论文中提取关键信息有助于研究人员提高工作效率并加快科学进展的速度。在过去几年里，科学信息提取（SciIE）的研究见证了数个新系统和基准的发布。然而，现有以论文为中心的数据集主要只关注论文的特定部分（例如摘要），且为单模态（即只有文本或表格），这是因为处理复杂性和高昂的注释费用所导致。此外，核心信息可能存在于文本或表格中，或者跨越二者。为弥补这一数据可用性的差距并实现跨模态信息提取，同时减轻标注成本，我们提出了一个半监督的流程，用于迭代地对文本中的实体、以及表格中的实体和关系进行注释。基于此流程，我们为科学界提供了一些新资源，包括一个高质量的基准、一个大型语料库和一个半监督的注释流程。我们进一步报告了最先进的信息提取模型在提出的基准数据集上的性能作为一个基准。最后，我们探索了像 ChatGPT 这样的大型语言模型在当前任务中的潜在能力。我们的新数据集、结果和分析验证了半监督流程的有效性和效率，同时我们也讨论了其剩余的局限性。