pwc-archive/papers-with-abstracts huggingface dataset:
Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval
Authors: Morris Florek, David Tschirschwitz, Björn Barz, Volker Rodehorst
Date: 2024-09-20 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval, Specificity

Current image retrieval systems often face domain specificity and generalization issues. This study aims to overcome these limitations by developing a computationally efficient training framework for a universal feature extractor that provides strong semantic image representations across various domains. To this end, we curated a multi-domain training dataset, called M4D-35k, which allows for resource-efficient training. Additionally, we conduct an extensive evaluation and comparison of various state-of-the-art visual-semantic foundation models and margin-based metric learning loss functions regarding their suitability for efficient universal feature extraction. Despite constrained computational resources, we achieve near state-of-the-art results on the Google Universal Image Embedding Challenge, with a mMP@5 of 0.721. This places our method at the second rank on the leaderboard, just 0.7 percentage points behind the best performing method. However, our model has 32% fewer overall parameters and 289 times fewer trainable parameters. Compared to methods with similar computational requirements, we outperform the previous state of the art by 3.3 percentage points. We release our code and M4D-35k training set annotations at https://github.com/morrisfl/UniFEx.
A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging
Authors: Savvas Sifnaios, George Arvanitakis, Fotios K. Konstantinidis, Georgios Tsimiklis, Angelos Amditis, Panayiotis Frangos
Date: 2024-09-20 00:00:00
Tasks: Material Classification

Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are tied to RGB-based systems, which are insufficient for applications in industries like waste sorting, pharmaceuticals, and defense, where advanced object characterization beyond shape or color is necessary. Hyperspectral (HS) imaging, capturing both spectral and spatial information, addresses these limitations and offers advantages over conventional technologies such as X-ray fluorescence and Raman spectroscopy, particularly in terms of speed, cost, and safety. This study evaluates the potential of combining HS imaging with deep learning for material characterization. The research involves: i) designing an experimental setup with HS camera, conveyor, and controlled lighting; ii) generating a multi-object dataset of various plastics (HDPE, PET, PP, PS) with semi-automated mask generation and Raman spectroscopy-based labeling; and iii) developing a deep learning model trained on HS images for pixel-level material classification. The model achieved 99.94\% classification accuracy, demonstrating robustness in color, size, and shape invariance, and effectively handling material overlap. Limitations, such as challenges with black objects, are also discussed. Extending computer vision beyond RGB to HS imaging proves feasible, overcoming major limitations of traditional methods and showing strong potential for future applications.
DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring
Authors: Ling Wang, Chen Wu, Lin Wang
Date: 2024-09-20 00:00:00
Tasks: Autonomous Vehicles, Deblurring, Depth Estimation, Model Optimization

Autonomous vehicles and robots often struggle with reliable visual perception at night due to the low illumination and motion blur caused by the long exposure time of RGB cameras. Existing methods address this challenge by sequentially connecting the off-the-shelf pretrained low-light enhancement and deblurring models. Unfortunately, these methods often lead to noticeable artifacts (\eg, color distortions) in the over-exposed regions or make it hardly possible to learn the motion cues of the dark regions. In this paper, we interestingly find vision-language models, \eg, Contrastive Language-Image Pretraining (CLIP), can comprehensively perceive diverse degradation levels at night. In light of this, we propose a novel transformer-based joint learning framework, named DAP-LED, which can jointly achieve low-light enhancement and deblurring, benefiting downstream tasks, such as depth estimation, segmentation, and detection in the dark. The key insight is to leverage CLIP to adaptively learn the degradation levels from images at night. This subtly enables learning rich semantic information and visual representation for optimization of the joint tasks. To achieve this, we first introduce a CLIP-guided cross-fusion module to obtain multi-scale patch-wise degradation heatmaps from the image embeddings. Then, the heatmaps are fused via the designed CLIP-enhanced transformer blocks to retain useful degradation information for effective model optimization. Experimental results show that, compared to existing methods, our DAP-LED achieves state-of-the-art performance in the dark. Meanwhile, the enhanced results are demonstrated to be effective for three downstream tasks. For demo and more results, please check the project page: \url{https://vlislab22.github.io/dap-led/}.
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Authors: Chinmay Rao, Matthias Van Osch, Nicola Pezzotti, Jeroen de Bresser, Laurens Beljaards, Jakob Meineke, Elwin de Weerdt, Huangling Lu, Mariya Doneva, Marius Staring
Date: 2024-09-20 00:00:00
Tasks: Anatomy, Diagnostic, Disentanglement, MRI Reconstruction, SSIM

Since multiple MRI contrasts of the same anatomy contain redundant information, one contrast can guide the reconstruction of an undersampled subsequent contrast. To this end, several end-to-end learning-based guided reconstruction methods have been proposed. However, a key challenge is the requirement of large paired training datasets comprising raw data and aligned reference images. We propose a modular two-stage approach addressing this issue, additionally providing an explanatory framework for the multi-contrast problem based on the shared and non-shared generative factors underlying two given contrasts. A content/style model of two-contrast image data is learned from a largely unpaired image-domain dataset and is subsequently applied as a plug-and-play operator in iterative reconstruction. The disentanglement of content and style allows explicit representation of contrast-independent and contrast-specific factors. Consequently, incorporating prior information into the reconstruction reduces to a simple replacement of the aliased content of the reconstruction iterate with high-quality content derived from the reference scan. Combining this component with a data consistency step and introducing a general corrective process for the content yields an iterative scheme. We name this novel approach PnP-CoSMo. Various aspects like interpretability and convergence are explored via simulations. Furthermore, its practicality is demonstrated on the NYU fastMRI DICOM dataset, showing improved generalizability compared to end-to-end methods, and on two in-house multi-coil raw datasets, offering up to 32.6% more acceleration over learning-based non-guided reconstruction for a given SSIM. In a small radiological task, PnP-CoSMo allowed 33.3% more acceleration over clinical reconstruction at diagnostic quality.
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Authors: Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher, Tim Holland-Letz, Harald Kittler, Philipp Tschandl, Markus V. Heppt, Carola Berking, Jochen S. Utikal, Bastian Schilling, Claudia Buerger, Cristian Navarrete-Dechent, Matthias Goebeler, Jakob Nikolas Kather, Carolin V. Schneider, Benjamin Durani, Hendrike Durani, Martin Jansen, Juliane Wacker, Joerg Wacker, Reader Study Consortium, Titus J. Brinker
Date: 2024-09-20 00:00:00
Tasks: Diagnostic, Melanoma Diagnosis

Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.
PLOT: Text-based Person Search with Part Slot Attention for Corresponding Part Discovery
Authors: Jicheol Park, Dongwon Kim, Boseung Jeong, Suha Kwak
Date: 2024-09-20 00:00:00
Tasks: Person Search, Retrieval, Text based Person Search

Text-based person search, employing free-form text queries to identify individuals within a vast image collection, presents a unique challenge in aligning visual and textual representations, particularly at the human part level. Existing methods often struggle with part feature extraction and alignment due to the lack of direct part-level supervision and reliance on heuristic features. We propose a novel framework that leverages a part discovery module based on slot attention to autonomously identify and align distinctive parts across modalities, enhancing interpretability and retrieval accuracy without explicit part-level correspondence supervision. Additionally, text-based dynamic part attention adjusts the importance of each part, further improving retrieval outcomes. Our method is evaluated on three public benchmarks, significantly outperforming existing methods.
Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks
Authors: Guibiao Liao, Wei Gao
Date: 2024-09-20 00:00:00
Tasks: Benchmarking, object-detection, Object Detection, Representation Learning, Salient Object Detection

Salient object detection (SOD) has achieved substantial progress in recent years. In practical scenarios, compressed images (CI) serve as the primary medium for data transmission and storage. However, scant attention has been directed towards SOD for compressed images using convolutional neural networks (CNNs). In this paper, we are dedicated to strictly benchmarking and analyzing CNN-based salient object detection on compressed images. To comprehensively study this issue, we meticulously establish various CI SOD datasets from existing public SOD datasets. Subsequently, we investigate representative CNN-based SOD methods, assessing their robustness on compressed images (approximately 2.64 million images). Importantly, our evaluation results reveal two key findings: 1) current state-of-the-art CNN-based SOD models, while excelling on clean images, exhibit significant performance bottlenecks when applied to compressed images. 2) The principal factors influencing the robustness of CI SOD are rooted in the characteristics of compressed images and the limitations in saliency feature learning. Based on these observations, we propose a simple yet promising baseline framework that focuses on robust feature representation learning to achieve robust CNN-based CI SOD. Extensive experiments demonstrate the effectiveness of our approach, showcasing markedly improved robustness across various levels of image degradation, while maintaining competitive accuracy on clean data. We hope that our benchmarking efforts, analytical insights, and proposed techniques will contribute to a more comprehensive understanding of the robustness of CNN-based SOD algorithms, inspiring future research in the community.
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
Authors: Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe, Stefan Wermter, Diedrich Wolter
Date: 2024-09-20 00:00:00

Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising field of research, since explanations that refer to concepts (i.e., semantically meaningful parts in an image) are intuitive to understand and go beyond saliency-based techniques that only reveal relevant regions. Given the remarkable progress in this field in recent years, it is time for the community to take a critical look at the advances and trends. Consequently, this paper reviews C-XAI methods to identify interesting and underexplored areas and proposes future research directions. To this end, we consider three main directions: the choice of concepts to explain, the choice of concept representation, and how we can control concepts. For the latter, we propose techniques and draw inspiration from the field of knowledge representation and learning, showing how this could enrich future C-XAI research.
Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models
Authors: Jordi Malé, Juan Fortea, Mateus Rozalem Aranha, Yann Heuzé, Neus Martínez-Abadías, Xavier Sevillano
Date: 2024-09-20 00:00:00
Tasks: Anatomy, Anomaly Detection

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointing regions of interest to unravel the neuroanatomical underpinnings of cognitive impairment and memory deficits. However, the connections between brain anatomy, cognitive performance and comorbidities like Alzheimer's disease are still poorly understood in the Down syndrome population. The latest advances in artificial intelligence constitute an opportunity for developing automatic tools to analyze large volumes of brain magnetic resonance imaging scans, overcoming the bottleneck of manual analysis. In this study, we propose the use of generative models for detecting brain alterations in people with Down syndrome affected by various degrees of neurodegeneration caused by Alzheimer's disease. To that end, we evaluate state-of-the-art brain anomaly detection models based on Variational Autoencoders and Diffusion Models, leveraging a proprietary dataset of brain magnetic resonance imaging scans. Following a comprehensive evaluation process, our study includes several key analyses. First, we conducted a qualitative evaluation by expert neuroradiologists. Second, we performed both quantitative and qualitative reconstruction fidelity studies for the generative models. Third, we carried out an ablation study to examine how the incorporation of histogram post-processing can enhance model performance. Finally, we executed a quantitative volumetric analysis of subcortical structures. Our findings indicate that some models effectively detect the primary alterations characterizing Down syndrome's brain anatomy, including a smaller cerebellum, enlarged ventricles, and cerebral cortex reduction, as well as the parietal lobe alterations caused by Alzheimer's disease.
Leveraging Text Localization for Scene Text Removal via Text-aware Masked Image Modeling
Authors: Zixiao Wang, Hongtao Xie, Yuxin Wang, Yadong Qu, Fengjun Guo, Pengwei Liu
Date: 2024-09-20 00:00:00
Tasks: Text Detection

Existing scene text removal (STR) task suffers from insufficient training data due to the expensive pixel-level labeling. In this paper, we aim to address this issue by introducing a Text-aware Masked Image Modeling algorithm (TMIM), which can pretrain STR models with low-cost text detection labels (e.g., text bounding box). Different from previous pretraining methods that use indirect auxiliary tasks only to enhance the implicit feature extraction ability, our TMIM first enables the STR task to be directly trained in a weakly supervised manner, which explores the STR knowledge explicitly and efficiently. In TMIM, first, a Background Modeling stream is built to learn background generation rules by recovering the masked non-text region. Meanwhile, it provides pseudo STR labels on the masked text region. Second, a Text Erasing stream is proposed to learn from the pseudo labels and equip the model with end-to-end STR ability. Benefiting from the two collaborative streams, our STR model can achieve impressive performance only with the public text detection datasets, which greatly alleviates the limitation of the high-cost STR labels. Experiments demonstrate that our method outperforms other pretrain methods and achieves state-of-the-art performance (37.35 PSNR on SCUT-EnsText). Code will be available at https://github.com/wzx99/TMIM.
HMD$^2$: Environment-aware Motion Generation from Single Egocentric Head-Mounted Device
Authors: Vladimir Guzov, Yifeng Jiang, Fangzhou Hong, Gerard Pons-Moll, Richard Newcombe, C. Karen Liu, Yuting Ye, Lingni Ma
Date: 2024-09-20 00:00:00
Tasks: Motion Generation

This paper investigates the online generation of realistic full-body human motion using a single head-mounted device with an outward-facing color camera and the ability to perform visual SLAM. Given the inherent ambiguity of this setup, we introduce a novel system, HMD$^2$, designed to balance between motion reconstruction and generation. From a reconstruction standpoint, our system aims to maximally utilize the camera streams to produce both analytical and learned features, including head motion, SLAM point cloud, and image embeddings. On the generative front, HMD$^2$ employs a multi-modal conditional motion Diffusion model, incorporating a time-series backbone to maintain temporal coherence in generated motions, and utilizes autoregressive in-painting to facilitate online motion inference with minimal latency (0.17 seconds). Collectively, we demonstrate that our system offers a highly effective and robust solution capable of scaling to an extensive dataset of over 200 hours collected in a wide range of complex indoor and outdoor environments using publicly available smart glasses.
Occupancy-Based Dual Contouring
Authors: Jisung Hwang, Minhyuk Sung
Date: 2024-09-20 00:00:00
Tasks: 3D Reconstruction, GPU

We introduce a dual contouring method that provides state-of-the-art performance for occupancy functions while achieving computation times of a few seconds. Our method is learning-free and carefully designed to maximize the use of GPU parallelization. The recent surge of implicit neural representations has led to significant attention to occupancy fields, resulting in a wide range of 3D reconstruction and generation methods based on them. However, the outputs of such methods have been underestimated due to the bottleneck in converting the resulting occupancy function to a mesh. Marching Cubes tends to produce staircase-like artifacts, and most subsequent works focusing on exploiting signed distance functions as input also yield suboptimal results for occupancy functions. Based on Manifold Dual Contouring (MDC), we propose Occupancy-Based Dual Contouring (ODC), which mainly modifies the computation of grid edge points (1D points) and grid cell points (3D points) to not use any distance information. We introduce auxiliary 2D points that are used to compute local surface normals along with the 1D points, helping identify 3D points via the quadric error function. To search the 1D, 2D, and 3D points, we develop fast algorithms that are parallelizable across all grid edges, faces, and cells. Our experiments with several 3D neural generative models and a 3D mesh dataset demonstrate that our method achieves the best fidelity compared to prior works.
Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging
Authors: Jintao Ren, Muheng Li, Stine Sofia Korreman
Date: 2024-09-20 00:00:00
Tasks: Lesion Detection, Segmentation, Tumor Segmentation

This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET).
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Authors: Ruben Gonzalez-Perez, Francisco Lopez-Tiro, Ivan Reyes-Amezcua, Eduardo Falcon-Morales, Rosa-Maria Rodriguez-Gueant, Jacques Hubert, Michel Daudon, Gilberto Ochoa-Ruiz, Christian Daul
Date: 2024-09-20 00:00:00

Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing natural and synthetic images of CCD images, it is possible to train models capable of performing very well on unseen intra-operative data. Our results show that is possible to attain an improvement of 10% in terms of accuracy compared to a baseline model pre-trained only on ImageNet. Moreover, our results show an improvement of 6% for surface images and 10% for section images compared to a model train on CCD images only, which demonstrates the effectiveness of using synthetic images.
Learning To Help: Training Models to Assist Legacy Devices
Authors: Yu Wu, Anand Sarwate
Date: 2024-09-24 00:00:00

Machine learning models implemented in hardware on physical devices may be deployed for a long time. The computational abilities of the device may be limited and become outdated with respect to newer improvements. Because of the size of ML models, offloading some computation (e.g. to an edge cloud) can help such legacy devices. We cast this problem in the framework of learning with abstention (LWA) in which the expert (edge) must be trained to assist the client (device). Prior work on LWA trains the client assuming the edge is either an oracle or a human expert. In this work, we formalize the reverse problem of training the expert for a fixed (legacy) client. As in LWA, the client uses a rejection rule to decide when to offload inference to the expert (at a cost). We find the Bayes-optimal rule, prove a generalization bound, and find a consistent surrogate loss function. Empirical results show that our framework outperforms confidence-based rejection rules.