As a part of the perception results of intelligent driving systems, static
object detection (SOD) in 3D space provides crucial cues for driving
environment understanding. With the rapid deployment of deep neural networks
for SOD tasks, the demand for high-quality training samples soars. The
traditional, also reliable, way is manual labeling over the dense LiDAR point
clouds and reference images. Though most public driving datasets adopt this
strategy to provide SOD ground truth (GT), it is still expensive (requires
LiDAR scanners) and low-efficient (time-consuming and unscalable) in practice.
This paper introduces VRSO, a visual-centric approach for static object
annotation. VRSO is distinguished in low cost, high efficiency, and high
quality: (1) It recovers static objects in 3D space with only camera images as
input, and (2) manual labeling is barely involved since GT for SOD tasks is
generated based on an automatic reconstruction and annotation pipeline. (3)
Experiments on the Waymo Open Dataset show that the mean reprojection error
from VRSO annotation is only 2.6 pixels, around four times lower than the Waymo
labeling (10.6 pixels). Source code is available at:
this https URL

该论文介绍了 VRSO，一种用于静态物体注释的基于视觉的方法，它使用相机图像作为输入，可以在 3D 空间中恢复静态物体，并基于自动重建和注释流程生成用于静态对象检测任务的 GT，其平均重投影误差仅为 2.6 像素，约为 Waymo 标注的四分之一（10.6 像素）。

VRSO：用于静态物体标注的视觉中心重建

VRSO: Visual-Centric Reconstruction for Static Object Annotation

Data collection from manual labeling provides domain-specific and
task-aligned supervision for data-driven approaches, and a critical mass of
well-annotated resources is required to achieve reasonable performance in
natural language processing tasks. However, manual annotations are often
challenging to scale up in terms of time and budget, especially when domain
knowledge, capturing subtle semantic features, and reasoning steps are needed.
In this paper, we investigate the efficacy of leveraging large language models
on automated labeling for computational stance detection. We empirically
observe that while large language models show strong potential as an
alternative to human annotators, their sensitivity to task-specific
instructions and their intrinsic biases pose intriguing yet unique challenges
in machine annotation. We introduce a multi-label and multi-target sampling
strategy to optimize the annotation quality. Experimental results on the
benchmark stance detection corpora show that our method can significantly
improve performance and learning efficacy.

研究了利用大型语言模型进行自动标注来进行计算立场检测，引入了多标签和多目标采样策略以优化标注质量，实验证明该方法能明显提高性能和学习效果。

计算立场检测的多标签和多目标机器注释采样

Multi-label and Multi-target Sampling of Machine Annotation for  Computational Stance Detection

Conventional video segmentation approaches rely heavily on appearance models.
Such methods often use appearance descriptors that have limited discriminative
power under complex scenarios. To improve the segmentation performance, this
paper presents a pyramid histogram based confidence map that incorporates
structure information into appearance statistics. It also combines geodesic
distance based dynamic models. Then, it employs an efficient measure of
uncertainty propagation using local classifiers to determine the image regions
where the object labels might be ambiguous. The final foreground cutout is
obtained by refining on the uncertain regions. Additionally, to reduce manual
labeling, our method determines the frames to be labeled by the human operator
in a principled manner, which further boosts the segmentation performance and
minimizes the labeling effort. Our extensive experimental analyses on two big
benchmarks demonstrate that our solution achieves superior performance,
favorable computational efficiency, and reduced manual labeling in comparison
to the state-of-the-art.

本文提出了一种基于金字塔直方图的置信度图和基于测地距离的动态模型的视频分割方法，使用本地分类器进行不确定性传递来确定图像区域，并通过对不确定区域的精细化加工得到前景分割结果。此方法能极大地减少手动标注，提高分割性能。在两个标准测试集上进行的实验表明，该方法具有卓越的性能，并且相对于现有技术具有较好的计算效率和减少手动标注的性质。