AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry

AI-Generated Images as Data Source: The Dawn of Synthetic Era

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .Comment: 20 pages, 11 figure

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Fine-Grained Image Analysis with Deep Learning: A Survey

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.Comment: Accepted by IEEE TPAM

Cross View Action Recognition

openCross View Action Recognition (CVAR) appraises a system's ability to recognise actions from viewpoints that are unfamiliar to the system. The state of the art methods that train on large amounts of training data rely on variation in the training data itself to increase their ability to tackle viewpoints changes. Therefore, these methods not only require a large scale dataset of appropriate classes for the application every time they train, but also correspondingly large amount of computation power for the training process leading to high costs, in terms of time, effort, funds and electrical energy. In this thesis, we propose a methodological pipeline that tackles change in viewpoint, training on small datasets and employing sustainable amounts of resources. Our method uses the optical flow input with a stream of a pre-trained model as-is to obtain a feature. Thereafter, this feature is used to train a custom designed classifier that promotes view-invariant properties. Our method only uses video information as input, in contrast to another set of methods that approach CVAR by using depth or pose input at the expense of increased sensor costs. We present a number of comparative analysis that aided the design of the pipelines, farther assessing the power of each component in the pipeline. The technique can also be adopted to existing, trained classifiers, with minimal fine-tuning, as this work demonstrates by comparing classifiers including shallow classifiers, deep pre-trained classifiers and our proposed classifier trained from scratch. Additionally, we present a set of qualitative results that promote our understanding of the relationship between viewpoints in the feature-space.openXXXII CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - InformaticaGoyal, Gaurv

HUMAN ACTIVITY RECOGNITION FROM EGOCENTRIC VIDEOS AND ROBUSTNESS ANALYSIS OF DEEP NEURAL NETWORKS

In recent years, there has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing egocentric view, which is a first-person view providing the view of the environment as seen by the wearer. Using only IMU data limits the variety and complexity of the activities that can be detected. Deep machine learning has achieved great success in image and video processing in recent years. Neural network based models provide improved accuracy in multiple fields in computer vision. However, there has been relatively less work focusing on designing specific models to improve the performance of egocentric image/video tasks. As deep neural networks keep improving the accuracy in computer vision tasks, the robustness and resilience of the networks should be improved as well to make it possible to be applied in safety-crucial areas such as autonomous driving. Motivated by these considerations, in the first part of the thesis, the problem of human activity detection and classification from egocentric cameras is addressed. First, anew method is presented to count the number of footsteps and compute the total traveled distance by using the data from the IMU sensors and camera of a smart phone. By incorporating data from multiple sensor modalities, and calculating the length of each step, instead of using preset stride lengths and assuming equal-length steps, the proposed method provides much higher accuracy compared to commercially available step counting apps. After the application of footstep counting, more complicated human activities, such as steps of preparing a recipe and sitting on a sofa, are taken into consideration. Multiple classification methods, non-deep learning and deep-learning-based, are presented, which employ both ego-centric camera and IMU data. Then, a Genetic Algorithm-based approach is employed to set the parameters of an activity classification network autonomously and performance is compared with empirically-set parameters. Then, a new framework is introduced to reduce the computational cost of human temporal activity recognition from egocentric videos while maintaining the accuracy at a comparable level. The actor-critic model of reinforcement learning is applied to optical flow data to locate a bounding box around region of interest, which is then used for clipping a sub-image from a video frame. A shallow and deeper 3D convolutional neural network is designed to process the original image and the clipped image region, respectively.Next, a systematic method is introduced that autonomously and simultaneously optimizes multiple parameters of any deep neural network by using a bi-generative adversarial network (Bi-GAN) guiding a genetic algorithm(GA). The proposed Bi-GAN allows the autonomous exploitation and choice of the number of neurons for the fully-connected layers, and number of filters for the convolutional layers, from a large range of values. The Bi-GAN involves two generators, and two different models compete and improve each other progressively with a GAN-based strategy to optimize the networks during a GA evolution.In this analysis, three different neural network layers and datasets are taken into consideration: First, 3D convolutional layers for ModelNet40 dataset. We applied the proposed approach on a 3D convolutional network by using the ModelNet40 dataset. ModelNet is a dataset of 3D point clouds. The goal is to perform shape classification over 40shape classes. LSTM layers for UCI HAR dataset. UCI HAR dataset is composed of InertialMeasurement Unit (IMU) data captured during activities of standing, sitting, laying, walking, walking upstairs and walking downstairs. These activities were performed by 30 subjects, and the 3-axial linear acceleration and 3-axial angular velocity were collected at a constant rate of 50Hz. 2D convolutional layers for Chars74k Dataset. Chars74k dataset contains 64 classes(0-9, A-Z, a-z), 7705 characters obtained from natural images, 3410 hand-drawn characters using a tablet PC and 62992 synthesised characters from computer fonts giving a total of over 74K images. In the final part of the thesis, network robustness and resilience for neural network models is investigated from adversarial examples (AEs) and automatic driving conditions. The transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, explicit content detection, optical character recognition(OCR), and object detection are investigated. It represents the cybercriminal’s situation where an ensemble of different detection mechanisms need to be evaded all at once.Novel dispersion Reduction(DR) attack is designed, which is a practical attack that overcomes existing attacks’ limitation of requiring task-specific loss functions by targeting on the “dispersion” of internal feature map. In the autonomous driving scenario, the adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving is studied. A novel attack technique, tracker hijacking, that can effectively fool Multi-Object Tracking (MOT) using AEs on object detection is presented. Using this technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards

An Analysis on Adversarial Machine Learning: Methods and Applications

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to provide an analysis on these two sides of adversarial learning and harness their potential for improving the robustness and generalization of deep models. In the first part of the dissertation, we study the adversarial susceptibility of deep learning models. We provide an empirical analysis on the extent of vulnerability by proposing two adversarial attacks that explore the geometric and frequency-domain characteristics of inputs to manipulate deep decisions. Afterward, we formalize the susceptibility of deep networks using the first-order approximation of the predictions and extend the theory to the ensemble classification scheme. Inspired by theoretical findings, we formalize a reliable and practical defense against adversarial examples to robustify ensembles. We extend this part by investigating the shortcomings of \gls{at} and highlight that the popular momentum stochastic gradient descent, developed essentially for natural training, is not proper for optimization in adversarial training since it is not designed to be robust against the chaotic behavior of gradients in this setup. Motivated by these observations, we develop an optimization method that is more suitable for adversarial training. In the second part of the dissertation, we harness adversarial learning to enhance the generalization and performance of deep networks in discriminative and generative tasks. We develop several models for biometric identification including fingerprint distortion rectification and latent fingerprint reconstruction. In particular, we develop a ridge reconstruction model based on generative adversarial networks that estimates the missing ridge information in latent fingerprints. We introduce a novel modification that enables the generator network to preserve the ID information during the reconstruction process. To address the scarcity of data, {\it e.g.}, in latent fingerprint analysis, we develop a supervised augmentation technique that combines input examples based on their salient regions. Our findings advocate that adversarial learning improves the performance and reliability of deep networks in a wide range of applications

Multi-modal Machine Learning in Engineering Design: A Review and Future Directions

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed