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TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
May 06, 2024
Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity and locality of patterns in time series data (e.g., diseases-related anomalous points in ECG). To address this challenge, we formally reformulate MTSC as a weakly supervised problem, introducing a novel multiple-instance learning (MIL) framework for better localization of patterns of interest and modeling time dependencies within time series. Our novel approach, TimeMIL, formulates the temporal correlation and ordering within a time-aware MIL pooling, leveraging a tokenized transformer with a specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art methods, underscoring the effectiveness of the weakly supervised TimeMIL in MTSC.
Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters
May 05, 2024Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method's significant potential in reconstruction under an uncertain forward model.
Motor Focus: Ego-Motion Prediction with All-Pixel Matching
Apr 25, 2024Motion analysis plays a critical role in various applications, from virtual reality and augmented reality to assistive visual navigation. Traditional self-driving technologies, while advanced, typically do not translate directly to pedestrian applications due to their reliance on extensive sensor arrays and non-feasible computational frameworks. This highlights a significant gap in applying these solutions to human users since human navigation introduces unique challenges, including the unpredictable nature of human movement, limited processing capabilities of portable devices, and the need for directional responsiveness due to the limited perception range of humans. In this project, we introduce an image-only method that applies motion analysis using optical flow with ego-motion compensation to predict Motor Focus-where and how humans or machines focus their movement intentions. Meanwhile, this paper addresses the camera shaking issue in handheld and body-mounted devices which can severely degrade performance and accuracy, by applying a Gaussian aggregation to stabilize the predicted motor focus area and enhance the prediction accuracy of movement direction. This also provides a robust, real-time solution that adapts to the user's immediate environment. Furthermore, in the experiments part, we show the qualitative analysis of motor focus estimation between the conventional dense optical flow-based method and the proposed method. In quantitative tests, we show the performance of the proposed method on a collected small dataset that is specialized for motor focus estimation tasks.
Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication
Apr 10, 2024Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low illumination, winding roads, and crowded traffic. To cover the limitations of former methods, in this paper, we propose a novel approach to realize an optimized CP under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range and enhance the Object Detection (OD) accuracy of the ego vehicle. In this two-step process, we first select the helper vehicles that contribute the most to CP based on their visual range and lowest motion blur. Next, we implement a radio block optimization among the candidate vehicles to further improve communication efficiency. We specifically focus on pedestrian detection as an exemplary scenario. To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception in challenging scenarios, substantially improving driving safety under adverse conditions. Finally, we note that the networking assumptions are adopted from LTE Release 14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V) communication. Nonetheless, our method is flexible and applicable to arbitrary V2V communications.
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation
Mar 19, 2024
This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation. With the assistance of the state-of-the-art real-time open-world object detection model Yolo-World and specialized prompts, the proposed framework can identify anomalies within camera-captured frames that include any possible obstacles, then generate concise, audio-delivered descriptions emphasizing abnormalities, assist in safe visual navigation in complex circumstances. Moreover, our proposed framework leverages the advantages of LLMs and the open-vocabulary object detection model to achieve the dynamic scenario switch, which allows users to transition smoothly from scene to scene, which addresses the limitation of traditional visual navigation. Furthermore, this paper explored the performance contribution of different prompt components, provided the vision for future improvement in visual accessibility, and paved the way for LLMs in video anomaly detection and vision-language understanding.
FLAME Diffuser: Grounded Wildfire Image Synthesis using Mask Guided Diffusion
Mar 06, 2024
The rise of machine learning in recent years has brought benefits to various research fields such as wide fire detection. Nevertheless, small object detection and rare object detection remain a challenge. To address this problem, we present a dataset automata that can generate ground truth paired datasets using diffusion models. Specifically, we introduce a mask-guided diffusion framework that can fusion the wildfire into the existing images while the flame position and size can be precisely controlled. In advance, to fill the gap that the dataset of wildfire images in specific scenarios is missing, we vary the background of synthesized images by controlling both the text prompt and input image. Furthermore, to solve the color tint problem or the well-known domain shift issue, we apply the CLIP model to filter the generated massive dataset to preserve quality. Thus, our proposed framework can generate a massive dataset of that images are high-quality and ground truth-paired, which well addresses the needs of the annotated datasets in specific tasks.
Driving Towards Inclusion: Revisiting In-Vehicle Interaction in Autonomous Vehicles
Jan 26, 2024This paper presents a comprehensive literature review of the current state of in-vehicle human-computer interaction (HCI) in the context of self-driving vehicles, with a specific focus on inclusion and accessibility. This study's aim is to examine the user-centered design principles for inclusive HCI in self-driving vehicles, evaluate existing HCI systems, and identify emerging technologies that have the potential to enhance the passenger experience. The paper begins by providing an overview of the current state of self-driving vehicle technology, followed by an examination of the importance of HCI in this context. Next, the paper reviews the existing literature on inclusive HCI design principles and evaluates the effectiveness of current HCI systems in self-driving vehicles. The paper also identifies emerging technologies that have the potential to enhance the passenger experience, such as voice-activated interfaces, haptic feedback systems, and augmented reality displays. Finally, the paper proposes an end-to-end design framework for the development of an inclusive in-vehicle experience, which takes into consideration the needs of all passengers, including those with disabilities, or other accessibility requirements. This literature review highlights the importance of user-centered design principles in the development of HCI systems for self-driving vehicles and emphasizes the need for inclusive design to ensure that all passengers can safely and comfortably use these vehicles. The proposed end-to-end design framework provides a practical approach to achieving this goal and can serve as a valuable resource for designers, researchers, and policymakers in this field.
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Jan 04, 2024Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation
Nov 30, 2023Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane components is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires manual work in identifying and segmenting CMs and capillaries. Here, we developed an image analysis tool, AutoQC, to automatically identify and segment CMs and capillaries in immunofluorescence images of collagen type IV, a predominant basement membrane protein within the myocardium. In addition, commonly used capillarization-related measurements can be derived from segmentation masks. AutoQC features a weakly supervised instance segmentation algorithm by leveraging the power of a pre-trained segmentation model via prompt engineering. AutoQC outperformed YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Furthermore, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations, leading to a reduced workload during network training. AutoQC provides an automated solution for quantifying cardiac capillarization in basement-membrane-immunostained myocardial slices, eliminating the need for manual image analysis once it is trained.
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning
Aug 25, 2023
In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator's reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator's end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, skyscrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episodes