With the growing capabilities of modern object detection networks and
datasets to train them, it has gotten more straightforward and, importantly,
less laborious to get up and running with a model that is quite adept at
detecting any number of various objects. However, while image datasets for
object detection have grown and continue to proliferate (the current most
extensive public set, ImageNet, contains over 14m images with over 14m
instances), the same cannot be said for textual caption datasets. While they
have certainly been growing in recent years, caption datasets present a much
more difficult challenge due to language differences, grammar, and the time it
takes for humans to generate them. Current datasets have certainly provided
many instances to work with, but it becomes problematic when a captioner may
have a more limited vocabulary, one may not be adequately fluent in the
language, or there are simple grammatical mistakes. These difficulties are
increased when the images get more specific, such as remote sensing images.
This paper aims to address this issue of potential information and
communication shortcomings in caption datasets. To provide a more precise
analysis, we specify our domain of images to be remote sensing images in the
RSICD dataset and experiment with the captions provided here. Our findings
indicate that ChatGPT grammar correction is a simple and effective way to
increase the performance accuracy of caption models by making data captions
more diverse and grammatically correct.

通过使用遥感图像领域的 RSICD 数据集中提供的字幕，本文旨在解决字幕数据集中可能存在的信息和沟通缺陷问题，并通过进行 ChatGPT 语法纠正来增加字幕模型的性能准确性。

卫星字幕：大规模语言模型助力标注

Satellite Captioning: Large Language Models to Augment Labeling

We present a task-aware approach to synthetic data generation. Our framework
employs a trainable synthesizer network that is optimized to produce meaningful
training samples by assessing the strengths and weaknesses of a `target'
network. The synthesizer and target networks are trained in an adversarial
manner wherein each network is updated with a goal to outdo the other.
Additionally, we ensure the synthesizer generates realistic data by pairing it
with a discriminator trained on real-world images. Further, to make the target
classifier invariant to blending artefacts, we introduce these artefacts to
background regions of the training images so the target does not over-fit to
them.
We demonstrate the efficacy of our approach by applying it to different
target networks including a classification network on AffNIST, and two object
detection networks (SSD, Faster-RCNN) on different datasets. On the AffNIST
benchmark, our approach is able to surpass the baseline results with just half
the training examples. On the VOC person detection benchmark, we show
improvements of up to 2.7% as a result of our data augmentation. Similarly on
the GMU detection benchmark, we report a performance boost of 3.5% in mAP over
the baseline method, outperforming the previous state of the art approaches by
up to 7.5% on specific categories.

本文提出了一种针对任务的合成数据生成方法，通过训练可训练的合成器网络产生有意义的样本，并与现实世界的图像相关的判别器配对来生成逼真的数据，再通过加入混合术来提高训练目标分类器的不变性，实验结果表明该方法在不同的目标网络上的应用可以显著提高检测性能。

通过合成学习生成合成数据

Learning to Generate Synthetic Data via Compositing

Human activity recognition is typically addressed by detecting key concepts
like global and local motion, features related to object classes present in the
scene, as well as features related to the global context. The next open
challenges in activity recognition require a level of understanding that pushes
beyond this and call for models with capabilities for fine distinction and
detailed comprehension of interactions between actors and objects in a scene.
We propose a model capable of learning to reason about semantically meaningful
spatiotemporal interactions in videos. The key to our approach is a choice of
performing this reasoning at the object level through the integration of state
of the art object detection networks. This allows the model to learn detailed
spatial interactions that exist at a semantic, object-interaction relevant
level. We evaluate our method on three standard datasets (Twenty-BN
Something-Something, VLOG and EPIC Kitchens) and achieve state of the art
results on all of them. Finally, we show visualizations of the interactions
learned by the model, which illustrate object classes and their interactions
corresponding to different activity classes.

本文讨论了利用深度学习模型进行人类活动识别研究中的挑战，并提出了一种基于对象感知网络的模型，以实现对视频中的语义意义的时空交互的推理，从而在三个不同的数据集上取得了最先进的结果。