Reflectance Transformation Imaging (RTI) is very popular for its ability to visually analyze surfaces by enhancing surface details through interactive relighting, starting from only a few tens of photographs taken with a fixed camera and variable illumination. Traditional methods like Polynomial Texture Maps (PTM) and Hemispherical Harmonics (HSH) are compact and fast, but struggle to accurately capture complex reflectance fields using few per-pixel coefficients and fixed bases, leading to artifacts, especially in highly reflective or shadowed areas. The NeuralRTI approach, which exploits a neural autoencoder to learn a compact function that better approximates the local reflectance as a function of light directions, has been shown to produce superior quality at comparable storage cost. However, as it performs interactive relighting with custom decoder networks with many parameters, the rendering step is computationally expensive and not feasible at full resolution for large images on limited hardware. Earlier attempts to reduce costs by directly training smaller networks have failed to produce valid results. For this reason, we propose to reduce its computational cost through a novel solution based on Knowledge Distillation (DisK-NeuralRTI). ...
Reflectance Prediction-based Knowledge Distillation for Robust 3D Object Detection in Compressed Point Clouds
School of Electronic Information Engineering, Taiyuan University of Science and Technology（太原科技大学电子信息工程学院）；School of Mechatronic Engineering and Automation, Shanghai University（上海大学机械电子工程与自动化学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Stein Eye Institute（斯坦眼科研究所）；VoxelCloud, Inc.（体素云公司）
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel…
Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage. This hinders their potential to assist vegetation monitoring or humanitarian actions, which require detecting rapid and detailed terrestrial surface changes. In this work, we probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics. We introduce a dataset of co-registered Moderate Resolution Imaging Spectroradiometer…
Null-Space Diffusion Distillation Unlocks Speed, Fidelity and Realism in Lensless Imaging
D3 Center, The University of Osaka（大阪大学D3中心）；The University of Osaka（大阪大学）；SANKEN, The University of Osaka（SANKEN与大阪大学）；Grad. Sch. of Eng. Sci., The University of Osaka（大阪大学工科科学院）
UT Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medicine（威尔康奈尔医学院）
AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy labelled datasets are sensitive to the noise type and lead to less generalization on the unseen samples. To address this challenge, we propose a Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a to…
DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation
Department of Computer Science, Colorado State University, USA（计算机科学系，科罗拉多州立大学）；Department of Soil and Crop Sciences, Colorado State University, USA（土壤与作物科学系，科罗拉多州立大学）
Reflectance Transformation Imaging (RTI) is a popular technique that allows the recovery of per-pixel reflectance information by capturing an object under different light conditions. This can be later used to reveal surface details and interactively relight the subject. Such process, however, typically requires dedicated hardware setups to recover the light direction from multiple locations, making the process tedious when performed outside the lab. We propose a novel RTI method that can be carried out by recording videos with two ordinary smartphones. The flash led-light of one device is used…
Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
Collège de France（法兰西学院）；Université PSL（巴黎文理研究大学）；Université Grenoble-Alpes（格勒诺布尔-阿尔卑斯大学）；Sorbonne University（索邦大学）；National Institutes of Health（美国国立卫生研究院）；Hospices Civils de Lyon（里昂公立医院）；Claude Bernard Lyon 1 University（里昂第一大学克洛德·贝尔纳）
A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东超高清沉浸媒体技术省重点实验室）；Shenzhen Graduate School, Peking University（北京大学深圳研究生院）
Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（光电国立实验室、华中科技大学）；Changzhou United Imaging Surgical Co., Ltd（常州联合影像手术有限公司）；Tongji Medical College of Huazhong University of Science and Technology（华中科技大学同济医学院）
Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI
Dublin City University（都柏林城市大学）；Insight Research Ireland Centre for Data Analytics（爱尔兰Insight研究数据分析师中心）；St. Vincent’s University Hospital（圣文森医院）；University College Dublin（都柏林大学）
Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection
Faculty of Mathematics and Computer Science, University of Bucharest（布加勒斯特大学数学与计算机科学学院）；Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy（罗马尼亚科学院格奥尔基·米霍茨-凯奥斯·伊阿科夫学院数学统计与应用数学研究所）
University of Amsterdam（阿姆斯特丹大学）；DUniversum（3D宇宙公司）；AkzoNobel（阿克苏诺贝尔）
Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to not being able to include all possible imaging conditions). In this paper, a combination of the two is proposed. Component specific priors like semantics and invariant features are exploited to obtain semantically and physically plausible re…
Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation
University of Ibadan（伊巴迪安大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；University College London（伦敦大学学院）；Hertie Institute for Brain Health（海德研究所）
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. Methods: The key idea of our approach called cross modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segm…
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））
Pothole classification has become an important task for road inspection vehicles to save drivers from potential car accidents and repair bills. Given the limited computational power and fixed number of training epochs, we propose iterative self knowledge distillation (ISKD) to train lightweight pothole classifiers. Designed to improve both the teacher and student models over time in knowledge distillation, ISKD outperforms the state-of-the-art self knowledge distillation method on three pothole classification datasets across four lightweight network architectures, which supports that self know…
Courant Institute of Mathematical Sciences（库朗数学科学研究所）；New York University（纽约大学）；Vilcek Institute of Graduate Biomedical Sciences（维尔切克生物医学研究生科学研究所）
Multi-modal magnetic resonance imaging (MRI) is a crucial method for analyzing the human brain. It is usually used for diagnosing diseases and for making valuable decisions regarding the treatments - for instance, checking for gliomas in the human brain. With varying degrees of severity and detection, properly diagnosing gliomas is one of the most daunting and significant analysis tasks in modern-day medicine. Our primary focus is on working with different approaches to perform the segmentation of brain tumors in multimodal MRI scans. Now, the quantity, variability of the data used for trainin…
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
Department of Computer Science and Engineering, University of Nevada, Reno（计算机科学与工程系，内华达大学雷诺分校）；Centre for Frontier AI Research (CFAR), and Institute of High Performance Computing (IHPC), A*STAR, Singapore（前沿人工智能研究中心（CFAR）和高性能计算研究所（IHPC），A*STAR，新加坡）
Xiangtan University（湘潭大学）；The University of Liverpool（利物浦大学）
Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments. {In this work, to tackle explainability and generalizability, we propose a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework.} The combined approac…
Nanyang Technological University（南洋理工大学）；Institute of Bioengineering and Bioimaging, A*STAR（A*STAR生物工程与生物成像研究所）
Staining is critical to cell imaging and medical diagnosis, which is expensive, time-consuming, labor-intensive, and causes irreversible changes to cell tissues. Recent advances in deep learning enabled digital staining via supervised model training. However, it is difficult to obtain large-scale stained/unstained cell image pairs in practice, which need to be perfectly aligned with the supervision. In this work, we propose a novel unsupervised deep learning framework for the digital staining of cell images using knowledge distillation and generative adversarial networks (GANs). A teacher mode…
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition
Northwestern Polytechnical University（西北工业大学）；Xijing Hospital, The Fourth Military Medical University（第四军医大学西京医院）；Beijing Technology And Business University（北京工商大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Nankai University（南开大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Monash University（莫纳什大学）；Airdoc；Kyushu Institute of Technology（九州工业大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD and DR share some common features like hemorrhagic points and exudation but most classification algorithms only train those disease models independently. Inspired by knowledge distillation where additional monitoring signals from various sour…
Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging-based classification of rare diseases is challenging due to the severe shortage in training examples. Few-shot learning (FSL) methods tackle this challenge by extracting generalizable prior knowledge from a large base dataset of common diseases and normal controls, and transferring the knowledge to rare diseases. Yet, most existing methods require the base dataset to be labeled and do not make full use of the precious examples of the rare diseases. To this end, we propose in th…
University of Amsterdam（阿姆斯特丹大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electronic Health Records (EHRs), which contain rich information on patients and findings from clinicians. It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis. In this paper, we propose \textit{variational knowledge distillation} (VKD), which is a new probabilistic inference framework for disease …
Shanghai Jiao Tong University（上海交通大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎理工学院）
The joint use of multiple imaging modalities for medical image segmentation has been widely studied in recent years. The fusion of information from different modalities has demonstrated to improve the segmentation accuracy, with respect to mono-modal segmentations, in several applications. However, acquiring multiple modalities is usually not possible in a clinical setting due to a limited number of physicians and scanners, and to limit costs and scan time. Most of the time, only one modality is acquired. In this paper, we propose KD-Net, a framework to transfer knowledge from a trained multi-…
Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University（同济大学医学院 pulmonary and critical care medicine 部门，上海东方医院）；Queen Mary University of London（伦敦女王玛丽大学）；School of Health and Wellbeing, University of Glasgow（格拉斯哥大学健康与福祉学院）
Event-based Motion-Robust Accurate Shape Estimation for Mixed Reflectance Scenes
Department of Electrical Engineering, Rice University（电气工程系，里士大学）；Wyant College of Optical Sciences, University of Arizona（光学科学学院，亚利桑那大学）；Department of Electrical and Computer Engineering, Northwestern University（电气与计算机工程系，北western大学）；Department of Computer Science, Northwestern University（计算机科学系，北western大学）
This thesis proposes spatio-spectral techniques for hyperspectral image analysis. Adaptive spatio-spectral support and variable exposure hyperspectral imaging is demonstrated to improve spectral reflectance recovery from hyperspectral images. Novel spectral dimensionality reduction techniques have been proposed from the perspective of spectral only and spatio-spectral information preservation. It was found that the joint sparse and joint group sparse hyperspectral image models achieve lower reconstruction error and higher recognition accuracy using only a small subset of bands. Hyperspectral i…
We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization imaging with deep learning to achieve high quality estimate of 3D object shape (surface normals and depth) and SVBRDF using single-view polarization imaging under frontal flash illumination. In addition to acquired polarization images, we provide our d…
There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence…
DeepForest: Sensing Into Self-Occluding Volumes of Vegetation With Aerial Imaging
Institute of Computer Graphics, Johannes Kepler University Linz（约翰内斯·开普勒林茨大学计算机图形学研究所）；Department of Remote Sensing, Helmholtz Centre for Environmental Research–UFZ（亥姆霍兹环境研究中心–UFZ遥感系）；Institute for Earth System Science and Remote Sensing, Leipzig University（莱比锡大学地球系统科学与遥感研究所）
This work presents the development of a flexible microscopic chemical imaging platform for cultural heritage that utilizes wavelength-tunable oblique illumination from a point source to obtain per-pixel reflectance spectra in the VIS-NIR range. The microscope light source can be adjusted on two axes allowing for a hemisphere of possible illumination directions. The synthesis of multiple illumination angles allows for the calculation of surface normal vectors, similar to phase gradients, and axial optical sectioning. The extraction of spectral reflectance images with high spatial resolutions fr…
Reflectance Multispectral Imaging for Soil Composition Estimation and USDA Texture Classification
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka（电子与电气工程系，珀达尼亚大学）；Multidisciplinary AI Research Center, University of Peradeniya, Peradeniya 20400, Sri Lanka（多学科人工智能研究中心，珀达尼亚大学）；Department of Electrical and Information Engineering, University of Ruhuna, Hapugala, Galle 80000, Sri Lanka（电子与信息工程系，鲁胡纳大学）；Department of Civil Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka（土木工程系，珀达尼亚大学）
