pwc-archive/papers-with-abstracts huggingface dataset:
Scale Equalization for Multi-Level Feature Fusion
Authors: Bum Jun Kim, Sang Woo Kim
Date: 2024-02-02 00:00:00
Tasks: Decoder, Semantic Segmentation

Deep neural networks have exhibited remarkable performance in a variety of computer vision fields, especially in semantic segmentation tasks. Their success is often attributed to multi-level feature fusion, which enables them to understand both global and local information from an image. However, we found that multi-level features from parallel branches are on different scales. The scale disequilibrium is a universal and unwanted flaw that leads to detrimental gradient descent, thereby degrading performance in semantic segmentation. We discover that scale disequilibrium is caused by bilinear upsampling, which is supported by both theoretical and empirical evidence. Based on this observation, we propose injecting scale equalizers to achieve scale equilibrium across multi-level features after bilinear upsampling. Our proposed scale equalizers are easy to implement, applicable to any architecture, hyperparameter-free, implementable without requiring extra computational cost, and guarantee scale equilibrium for any dataset. Experiments showed that adopting scale equalizers consistently improved the mIoU index across various target datasets, including ADE20K, PASCAL VOC 2012, and Cityscapes, as well as various decoder choices, including UPerHead, PSPHead, ASPPHead, SepASPPHead, and FCNHead.
Seeing Objects in a Cluttered World: Computational Objectness from Motion in Video
Authors: Douglas Poland, Amar Saini
Date: 2024-02-02 00:00:00
Tasks: Object

Perception of the visually disjoint surfaces of our cluttered world as whole objects, physically distinct from those overlapping them, is a cognitive phenomenon called objectness that forms the basis of our visual perception. Shared by all vertebrates and present at birth in humans, it enables object-centric representation and reasoning about the visual world. We present a computational approach to objectness that leverages motion cues and spatio-temporal attention using a pair of supervised spatio-temporal R(2+1)U-Nets. The first network detects motion boundaries and classifies the pixels at those boundaries in terms of their local foreground-background sense. This motion boundary sense (MBS) information is passed, along with a spatio-temporal object attention cue, to an attentional surface perception (ASP) module which infers the form of the attended object over a sequence of frames and classifies its 'pixels' as visible or obscured. The spatial form of the attention cue is flexible, but it must loosely track the attended object which need not be visible. We demonstrate the ability of this simple but novel approach to infer objectness from phenomenology without object models, and show that it delivers robust perception of individual attended objects in cluttered scenes, even with blur and camera shake. We show that our data diversity and augmentation minimizes bias and facilitates transfer to real video. Finally, we describe how this computational objectness capability can grow in sophistication and anchor a robust modular video object perception framework.
A Single Simple Patch is All You Need for AI-generated Image Detection
Authors: Jiaxuan Chen, Jieteng Yao, Li Niu
Date: 2024-02-02 00:00:00
Tasks: All

The recent development of generative models unleashes the potential of generating hyper-realistic fake images. To prevent the malicious usage of fake images, AI-generated image detection aims to distinguish fake images from real images. However, existing method suffer from severe performance drop when detecting images generated by unseen generators. We find that generative models tend to focus on generating the patches with rich textures to make the images more realistic while neglecting the hidden noise caused by camera capture present in simple patches. In this paper, we propose to exploit the noise pattern of a single simple patch to identify fake images. Furthermore, due to the performance decline when handling low-quality generated images, we introduce an enhancement module and a perception module to remove the interfering information. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on public benchmarks.
A Survey for Foundation Models in Autonomous Driving
Authors: Haoxiang Gao, Zhongruo Wang, Yaqian Li, Kaiwen Long, Ming Yang, Yiqing Shen
Date: 2024-02-02 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Code Generation, object-detection, Object Detection, Spatial Reasoning, Survey

The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD). This survey presents a comprehensive review of more than 40 research papers, demonstrating the role of foundation models in enhancing AD. Large language models contribute to planning and simulation in AD, particularly through their proficiency in reasoning, code generation and translation. In parallel, vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking, as well as creating realistic driving scenarios for simulation and testing. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning, crucial for end-to-end AD. This survey not only provides a structured taxonomy, categorizing foundation models based on their modalities and functionalities within the AD domain but also delves into the methods employed in current research. It identifies the gaps between existing foundation models and cutting-edge AD approaches, thereby charting future research directions and proposing a roadmap for bridging these gaps.
Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models
Authors: Mohsena Chowdhury, Tejas Vyas, Rahul Alapati, Andrés M Bur, Guanghui Wang
Date: 2024-02-01 00:00:00
Tasks: Deep Learning

Inspire therapy is an FDA-approved internal neurostimulation treatment for obstructive sleep apnea. However, not all patients respond to this therapy, posing a challenge even for experienced otolaryngologists to determine candidacy. This paper makes the first attempt to leverage both machine learning and deep learning techniques in discerning patient responsiveness to Inspire therapy using medical data and videos captured through Drug-Induced Sleep Endoscopy (DISE), an essential procedure for Inspire therapy. To achieve this, we gathered and annotated three datasets from 127 patients. Two of these datasets comprise endoscopic videos focused on the Base of the Tongue and Velopharynx. The third dataset composes the patient's clinical information. By utilizing these datasets, we benchmarked and compared the performance of six deep learning models and five classical machine learning algorithms. The results demonstrate the potential of employing machine learning and deep learning techniques to determine a patient's eligibility for Inspire therapy, paving the way for future advancements in this field.
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
Authors: Salman Ul Hassan Dar, Marvin Seyfarth, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg, Robert Malte Siepmann, Fabian Christopher Laqua, Jannik Kahmann, Norbert Frey, Bettina Baeßler, Sebastian Foersch, Daniel Truhn, Jakob Nikolas Kather, Sandy Engelhardt
Date: 2024-02-01 00:00:00
Tasks: Copy Detection, Data Augmentation, Image Generation, Memorization, Synthetic Data Generation

AI models present a wide range of applications in the field of medicine. However, achieving optimal performance requires access to extensive healthcare data, which is often not readily available. Furthermore, the imperative to preserve patient privacy restricts patient data sharing with third parties and even within institutes. Recently, generative AI models have been gaining traction for facilitating open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, some of these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. Considering the importance of the problem, surprisingly it has received relatively little attention in the medical imaging community. To this end, we assess memorization in unconditional latent diffusion models. We train latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation. We then detect the amount of training data memorized utilizing our novel self-supervised copy detection approach and further investigate various factors that can influence memorization. Our findings show a surprisingly high degree of patient data memorization across all datasets. Comparison with non-diffusion generative models, such as autoencoders and generative adversarial networks, indicates that while latent diffusion models are more susceptible to memorization, overall they outperform non-diffusion models in synthesis quality. Further analyses reveal that using augmentation strategies, small architecture, and increasing dataset can reduce memorization while over-training the models can enhance it. Collectively, our results emphasize the importance of carefully training generative models on private medical imaging datasets, and examining the synthetic data to ensure patient privacy before sharing it for medical research and applications.
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Authors: Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
Date: 2024-02-01 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance. However, few results exist addressing the convergence of such regularisation, particularly within the context of critical points as opposed to global minimisers. In this paper, we present a generalised formulation of convergent regularisation in terms of critical points, and show that this is achieved by a class of weakly convex regularisers. We prove convergence of the primal-dual hybrid gradient method for the associated variational problem, and, given a Kurdyka-Lojasiewicz condition, an $\mathcal{O}(\log{k}/k)$ ergodic convergence rate. Finally, applying this theory to learned regularisation, we prove universal approximation for input weakly convex neural networks (IWCNN), and show empirically that IWCNNs can lead to improved performance of learned adversarial regularisers for computed tomography (CT) reconstruction.
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
Authors: Zikang Leng, Amitrajit Bhattacharjee, Hrudhai Rajasekhar, Lizhe Zhang, Elizabeth Bruda, Hyeokhyen Kwon, Thomas Plötz
Date: 2024-02-01 00:00:00
Tasks: Activity Recognition, Diversity, Human Activity Recognition, Motion Synthesis

One of the primary challenges in the field of human activity recognition (HAR) is the lack of large labeled datasets. This hinders the development of robust and generalizable models. Recently, cross modality transfer approaches have been explored that can alleviate the problem of data scarcity. These approaches convert existing datasets from a source modality, such as video, to a target modality (IMU). With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT. In this work, we conduct a large-scale evaluation of language-based cross modality transfer to determine their effectiveness for HAR. Based on this study, we introduce two new extensions for IMUGPT that enhance its use for practical HAR application scenarios: a motion filter capable of filtering out irrelevant motion sequences to ensure the relevance of the generated virtual IMU data, and a set of metrics that measure the diversity of the generated data facilitating the determination of when to stop generating virtual IMU data for both effective and efficient processing. We demonstrate that our diversity metrics can reduce the effort needed for the generation of virtual IMU data by at least 50%, which open up IMUGPT for practical use cases beyond a mere proof of concept.
VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
Authors: Zelong Liu, Andrew Tieu, Nikhil Patel, Georgios Soultanidis, Louisa Deyer, Ying Wang, Sean Huver, Alexander Zhou, Yunhao Mei, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
Date: 2024-02-01 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Self-Supervised Learning, Semantic Segmentation

Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present VIsualization and Segmentation Masked AutoEncoder (VIS-MAE), novel model weights specifically designed for medical imaging. Specifically, VIS-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET,X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VIS-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications. In addition, VIS-MAE has improved label efficiency as it can achieve similar performance to other models with a reduced amount of labeled training data (50% or 80%) compared to other pre-trained weights. VIS-MAE represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload. The source code of this work is available at https://github.com/lzl199704/VIS-MAE.
MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
Authors: Alexander Zhou, Zelong Liu, Andrew Tieu, Nikhil Patel, Sean Sun, Anthony Yang, Peter Choi, Hao-Chih Lee, Mickael Tordjman, Louisa Deyer, Yunhao Mei, Valentin Fauveau, George Soultanidis, Bachir Taouli, Mingqian Huang, Amish Doshi, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
Date: 2024-02-01 00:00:00
Tasks: Anatomy, Benchmarking, Deep Learning, MRI segmentation, Segmentation

In this retrospective study, we annotated 44 structures on two datasets: an internal dataset of 1,518 MRI sequences from 843 patients at the Mount Sinai Health System, and an external dataset of 397 MRI sequences from 263 patients for benchmarking. The internal dataset trained the nnU-Net model MRAnnotator, which demonstrated strong generalizability on the external dataset. MRAnnotator outperformed existing models such as TotalSegmentator MRI and MRSegmentator on both datasets, achieving an overall average Dice score of 0.878 on the internal dataset and 0.875 on the external set. Model weights are available on GitHub, and the external test set can be shared upon request.
mmID: High-Resolution mmWave Imaging for Human Identification
Authors: Sakila S. Jayaweera, Sai Deepika Regani, Yuqian Hu, Beibei Wang, K. J. Ray Liu
Date: 2024-02-01 00:00:00
Tasks: Activity Recognition, Overall - Test, Pose Estimation

Achieving accurate human identification through RF imaging has been a persistent challenge, primarily attributed to the limited aperture size and its consequent impact on imaging resolution. The existing imaging solution enables tasks such as pose estimation, activity recognition, and human tracking based on deep neural networks by estimating skeleton joints. In contrast to estimating joints, this paper proposes to improve imaging resolution by estimating the human figure as a whole using conditional generative adversarial networks (cGAN). In order to reduce training complexity, we use an estimated spatial spectrum using the MUltiple SIgnal Classification (MUSIC) algorithm as input to the cGAN. Our system generates environmentally independent, high-resolution images that can extract unique physical features useful for human identification. We use a simple convolution layers-based classification network to obtain the final identification result. From the experimental results, we show that resolution of the image produced by our trained generator is high enough to enable human identification. Our finding indicates high-resolution accuracy with 5% mean silhouette difference to the Kinect device. Extensive experiments in different environments on multiple testers demonstrate that our system can achieve 93% overall test accuracy in unseen environments for static human target identification.
A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)
Authors: Kirolos Attallah, Girgis Zaky, Nourhan Abdelrhim, Kyrillos Botros, Amjad Dife, Nermin Negied
Date: 2024-02-01 00:00:00

Customers all over the world want to see how the clothes fit them or not before purchasing. Therefore, customers by nature prefer brick-and-mortar clothes shopping so they can try on products before purchasing them. But after the Pandemic of COVID19 many sellers either shifted to online shopping or closed their fitting rooms which made the shopping process hesitant and doubtful. The fact that the clothes may not be suitable for their buyers after purchase led us to think about using new AI technologies to create an online platform or a virtual fitting room (VFR) in the form of a mobile application and a deployed model using a webpage that can be embedded later to any online store where they can try on any number of cloth items without physically trying them. Besides, it will save much searching time for their needs. Furthermore, it will reduce the crowding and headache in the physical shops by applying the same technology using a special type of mirror that will enable customers to try on faster. On the other hand, from business owners' perspective, this project will highly increase their online sales, besides, it will save the quality of the products by avoiding physical trials issues. The main approach used in this work is applying Generative Adversarial Networks (GANs) combined with image processing techniques to generate one output image from two input images which are the person image and the cloth image. This work achieved results that outperformed the state-of-the-art approaches found in literature.
Compressed image quality assessment using stacking
Authors: S. Farhad Hosseini-Benvidi, Hossein Motamednia, Azadeh Mansouri, Mohammadreza Raei, Ahmad Mahmoudi-Aznaveh
Date: 2024-02-01 00:00:00
Tasks: Compressed Image Quality Assessment, Image Compression, Image Quality Assessment

It is well-known that there is no universal metric for image quality evaluation. In this case, distortion-specific metrics can be more reliable. The artifact imposed by image compression can be considered as a combination of various distortions. Depending on the image context, this combination can be different. As a result, Generalization can be regarded as the major challenge in compressed image quality assessment. In this approach, stacking is employed to provide a reliable method. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. Moreover, the results of the Full-Reference (FR) and No-Reference (NR) models are aggregated to improve the proposed Full-Reference method for compressed image quality evaluation. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6\%, which illustrates the effectiveness of the proposed fusion-based approach.
YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving
Authors: Annika Meyer, Christoph Stiller
Date: 2024-02-01 00:00:00
Tasks: Philosophy

In automated driving, highly accurate maps are commonly used to support and complement perception. These maps are costly to create and quickly become outdated as the traffic world is permanently changing. In order to support or replace the map of an automated system with detections from sensor data, a perception module must be able to detect the map features. We propose a neural network that follows the one shot philosophy of YOLO but is designed for detection of 1D structures in images, such as lane boundaries. We extend previous ideas by a midpoint based line representation and anchor definitions. This representation can be used to describe lane borders, markings, but also implicit features such as centerlines of lanes. The broad applicability of the approach is shown with the detection performance on lane centerlines, lane borders as well as the markings both on highways and in urban areas. Versatile lane boundaries are detected and can be inherently classified as dashed or solid lines, curb, road boundaries, or implicit delimitation.
Enhanced fringe-to-phase framework using deep learning
Authors: Won-Hoe Kim, Bongjoong Kim, Hyung-gun Chi, Jae-Sang Hyun
Date: 2024-02-01 00:00:00
Tasks: 3D Reconstruction, Deep Learning

In Fringe Projection Profilometry (FPP), achieving robust and accurate 3D reconstruction with a limited number of fringe patterns remains a challenge in structured light 3D imaging. Conventional methods require a set of fringe images, but using only one or two patterns complicates phase recovery and unwrapping. In this study, we introduce SFNet, a symmetric fusion network that transforms two fringe images into an absolute phase. To enhance output reliability, Our framework predicts refined phases by incorporating information from fringe images of a different frequency than those used as input. This allows us to achieve high accuracy with just two images. Comparative experiments and ablation studies validate the effectiveness of our proposed method. The dataset and code are publicly accessible on our project page https://wonhoe-kim.github.io/SFNet.
FuseFormer: A Transformer for Visual and Thermal Image Fusion
Authors: Aytekin Erdogan, Erdem Akagündüz
Date: 2024-02-01 00:00:00
Tasks: SSIM

Due to the lack of a definitive ground truth for the image fusion problem, the loss functions are structured based on evaluation metrics, such as the structural similarity index measure (SSIM). However, in doing so, a bias is introduced toward the SSIM and, consequently, the input visual band image. The objective of this study is to propose a novel methodology for the image fusion problem that mitigates the limitations associated with using classical evaluation metrics as loss functions. Our approach integrates a transformer-based multi-scale fusion strategy that adeptly addresses local and global context information. This integration not only refines the individual components of the image fusion process but also significantly enhances the overall efficacy of the method. Our proposed method follows a two-stage training approach, where an auto-encoder is initially trained to extract deep features at multiple scales in the first stage. For the second stage, we integrate our fusion block and change the loss function as mentioned. The multi-scale features are fused using a combination of Convolutional Neural Networks (CNNs) and Transformers. The CNNs are utilized to capture local features, while the Transformer handles the integration of general context features. Through extensive experiments on various benchmark datasets, our proposed method, along with the novel loss function definition, demonstrates superior performance compared to other competitive fusion algorithms.