Anticipating object state changes in images and videos is a challenging
problem whose solution has important implications in vision-based scene
understanding, automated monitoring systems, and action planning. In this work,
we propose the first method for solving this problem. The proposed method
predicts object state changes that will occur in the near future as a result of
yet unseen human actions. To address this new problem, we propose a novel
framework that integrates learnt visual features that represent the recent
visual information, with natural language (NLP) features that represent past
object state changes and actions. Leveraging the extensive and challenging
Ego4D dataset which provides a large-scale collection of first-person
perspective videos across numerous interaction scenarios, we introduce new
curated annotation data for the object state change anticipation task (OSCA),
noted as Ego4D-OSCA. An extensive experimental evaluation was conducted that
demonstrates the efficacy of the proposed method in predicting object state
changes in dynamic scenarios. The proposed work underscores the potential of
integrating video and linguistic cues to enhance the predictive performance of
video understanding systems. Moreover, it lays the groundwork for future
research on the new task of object state change anticipation. The source code
and the new annotation data (Ego4D-OSCA) will be made publicly available.

提出了解决图像和视频中物体状态变化的问题的第一种方法，通过集成学习的视觉特征和自然语言特征来预测未来可能发生的物体状态变化，从而增强视频理解系统的预测性能。

预测物体状态的变化

Anticipating Object State Changes

The rapid growth of the Industry 4.0 paradigm is increasing the pressure to
develop effective automated monitoring systems. Artificial Intelligence (AI) is
a convenient tool to improve the efficiency of industrial processes while
reducing errors and waste. In fact, it allows the use of real-time data to
increase the effectiveness of monitoring systems, minimize errors, make the
production process more sustainable, and save costs. In this paper, a novel
automatic monitoring system is proposed in the context of production process of
plastic consumables used in analysis laboratories, with the aim to increase the
effectiveness of the control process currently performed by a human operator.
In particular, we considered the problem of classifying the presence or absence
of a transparent anticoagulant substance inside test tubes. Specifically, a
hand-designed deep network model is used and compared with some
state-of-the-art models for its ability to categorize different images of vials
that can be either filled with the anticoagulant or empty. Collected results
indicate that the proposed approach is competitive with state-of-the-art models
in terms of accuracy. Furthermore, we increased the complexity of the task by
training the models on the ability to discriminate not only the presence or
absence of the anticoagulant inside the vial, but also the size of the test
tube. The analysis performed in the latter scenario confirms the
competitiveness of our approach. Moreover, our model is remarkably superior in
terms of its generalization ability and requires significantly fewer resources.
These results suggest the possibility of successfully implementing such a model
in the production process of a plastic consumables company.

提出了一种新型的自动监控系统，在分析实验室中生产过程中的塑料耗材，通过使用手动设计的深度网络模型来增强对试管内透明抗凝剂物质存在与否的控制过程的有效性，并通过实验结果证明该方法与最先进的模型相比具有竞争力，表现出优于其他模型的分类能力和泛化能力，为塑料耗材公司的生产过程成功实施类似模型提供了可能性。

一种基于计算机视觉的用于分析实验室耗材自动控制的质量评估技术

A Computer Vision-Based Quality Assessment Technique for the automatic  control of consumables for analytical laboratories

Livestock feeding behavior is an influential research area for those involved
in animal husbandry and agriculture. In recent years, there has been a growing
interest in automated systems for monitoring the behavior of ruminants. Despite
the developments accomplished in the last decade, there is still much to do and
learn about the methods for measuring and analyzing livestock feeding behavior.
Automated monitoring systems mainly use motion, acoustic, and image sensors to
collect animal behavioral data. The performance evaluation of existing methods
is a complex task and direct comparisons between studies are difficult. Several
factors prevent a direct comparison, starting from the diversity of data and
performance metrics used in the experiments. To the best of our knowledge, this
work represents the first tutorial-style review on the analysis of the feeding
behavior of ruminants, emphasizing the relationship between sensing
methodologies, signal processing and computational intelligence methods. It
assesses the main sensing methodologies (i.e. based on movement, sound,
images/videos and pressure) and the main techniques to measure and analyze the
signals associated with feeding behavior, evaluating their use in different
settings and situations. It also highlights the potentiality of automated
monitoring systems to provide valuable information that improves our
understanding of livestock feeding behavior. The relevance of these systems is
increasingly important due to their impact on production systems and research.
Finally, the paper closes by discussing future challenges and opportunities in
livestock feeding behavior monitoring.

畜牧饲料行为监测的自动化方法和技术的综述，讨论了不同的传感方法、信号处理和计算智能方法，并强调了自动监测系统提供有价值信息以改善我们对畜牧饲料行为的理解的潜力。