pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels
Authors: Nida Nasir, Mustafa Sameer, Feras Barneih, Omar Alshaltone, Muneeb Ahmed
Date: 2024-05-23 00:00:00
Tasks: Deep Learning, Photoplethysmography (PPG), Specificity

Continuous photoplethysmography (PPG)-based blood pressure monitoring is necessary for healthcare and fitness applications. In Artificial Intelligence (AI), signal classification levels with the machine and deep learning arrangements need to be explored further. Techniques based on time-frequency spectra, such as Short-time Fourier Transform (STFT), have been used to address the challenges of motion artifact correction. Therefore, the proposed study works with PPG signals of more than 200 patients (650+ signal samples) with hypertension, using STFT with various Neural Networks (Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), followed by machine learning classifiers, such as, Support Vector Machine (SVM) and Random Forest (RF). The classification has been done for two categories: Prehypertension (normal levels) and Hypertension (includes Stage I and Stage II). Various performance metrics have been obtained with two batch sizes of 3 and 16 for the fusion of the neural networks. With precision and specificity of 100% and recall of 82.1%, the LSTM model provides the best results among all combinations of Neural Networks. However, the maximum accuracy of 71.9% is achieved by the LSTM-CNN model. Further stacked Ensemble method has been used to achieve 100% accuracy for Meta-LSTM-RF, Meta- LSTM-CNN-RF and Meta- STFT-CNN-SVM.
Multistable Shape from Shading Emerges from Patch Diffusion
Authors: Xinran Nicole Han, Todd Zickler, Ko Nishino
Date: 2024-05-23 00:00:00
Tasks: Denoising

Models for inferring monocular shape of surfaces with diffuse reflection -- shape from shading -- ought to produce distributions of outputs, because there are fundamental mathematical ambiguities of both continuous (e.g., bas-relief) and discrete (e.g., convex/concave) types that are also experienced by humans. Yet, the outputs of current models are limited to point estimates or tight distributions around single modes, which prevent them from capturing these effects. We introduce a model that reconstructs a multimodal distribution of shapes from a single shading image, which aligns with the human experience of multistable perception. We train a small denoising diffusion process to generate surface normal fields from $16\times 16$ patches of synthetic images of everyday 3D objects. We deploy this model patch-wise at multiple scales, with guidance from inter-patch shape consistency constraints. Despite its relatively small parameter count and predominantly bottom-up structure, we show that multistable shape explanations emerge from this model for ambiguous test images that humans experience as being multistable. At the same time, the model produces veridical shape estimates for object-like images that include distinctive occluding contours and appear less ambiguous. This may inspire new architectures for stochastic 3D shape perception that are more efficient and better aligned with human experience.
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
Authors: Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Date: 2024-05-23 00:00:00
Tasks: Anomaly Detection, Anomaly Segmentation, Few-Shot Learning, Meta-Learning

Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision-language models. We affirm this by adapting DINOv2 for one-shot and few-shot anomaly detection, with a focus on industrial applications. We show that this approach does not only rival existing techniques but can even outmatch them in many settings. Our proposed vision-only approach, AnomalyDINO, is based on patch similarities and enables both image-level anomaly prediction and pixel-level anomaly segmentation. The approach is methodologically simple and training-free and, thus, does not require any additional data for fine-tuning or meta-learning. Despite its simplicity, AnomalyDINO achieves state-of-the-art results in one- and few-shot anomaly detection (e.g., pushing the one-shot performance on MVTec-AD from an AUROC of 93.1% to 96.6%). The reduced overhead, coupled with its outstanding few-shot performance, makes AnomalyDINO a strong candidate for fast deployment, e.g., in industrial contexts.
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks
Authors: Xingguang Jiang, Xiaofeng Bian, Chenggang Guo
Date: 2024-05-23 00:00:00
Tasks: Depth Estimation, Stereo Matching

Depth estimation based on stereo matching is a classic but popular computer vision problem, which has a wide range of real-world applications. Current stereo matching methods generally adopt the deep Siamese neural network architecture, and have achieved impressing performance by constructing feature matching cost volumes and using 3D convolutions for cost aggregation. However, most existing methods suffer from large number of parameters and slow running time due to the sequential use of 3D convolutions. In this paper, we propose Ghost-Stereo, a novel end-to-end stereo matching network. The feature extraction part of the network uses the GhostNet to form a U-shaped structure. The core of Ghost-Stereo is a GhostNet feature-based cost volume enhancement (Ghost-CVE) module and a GhostNet-inspired lightweight cost volume aggregation (Ghost-CVA) module. For the Ghost-CVE part, cost volumes are constructed and fused by the GhostNet-based features to enhance the spatial context awareness. For the Ghost-CVA part, a lightweight 3D convolution bottleneck block based on the GhostNet is proposed to reduce the computational complexity in this module. By combining with the context and geometry fusion module, a classical hourglass-shaped cost volume aggregate structure is constructed. Ghost-Stereo achieves a comparable performance than state-of-the-art real-time methods on several publicly benchmarks, and shows a better generalization ability.
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Authors: Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M. Saquib Sarfraz, Mohsen Ali
Date: 2024-05-23 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decision Making, Domain Generalization, Object, object-detection, Object Detection

In this work, we tackle the problem of domain generalization for object detection, specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the source domain and aligning detections based on class prediction confidence and localization. Firstly, we demonstrate that by carefully selecting a set of augmentations, a base detector can outperform existing methods for single domain generalization by a good margin. This highlights the importance of domain diversification in improving the performance of object detectors. Secondly, we introduce a method to align detections from multiple views, considering both classification and localization outputs. This alignment procedure leads to better generalized and well-calibrated object detector models, which are crucial for accurate decision-making in safety-critical applications. Our approach is detector-agnostic and can be seamlessly applied to both single-stage and two-stage detectors. To validate the effectiveness of our proposed methods, we conduct extensive experiments and ablations on challenging domain-shift scenarios. The results consistently demonstrate the superiority of our approach compared to existing methods. Our code and models are available at: https://github.com/msohaildanish/DivAlign
Scalable Visual State Space Model with Fractal Scanning
Authors: Lv Tang, Haoke Xiao, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Bo Li
Date: 2024-05-23 00:00:00
Tasks: image-classification, Image Classification, Mamba, model, State Space Models

Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. However, the Transformer's quadratic complexity poses challenges for handling longer sequences and higher resolution images. To address this challenge, State Space Models (SSMs) like Mamba have emerged as efficient alternatives, initially matching Transformer performance in NLP tasks and later surpassing Vision Transformers (ViTs) in various CV tasks. To improve the performance of SSMs, one crucial aspect is effective serialization of image patches. Existing methods, relying on linear scanning curves, often fail to capture complex spatial relationships and produce repetitive patterns, leading to biases. To address these limitations, we propose using fractal scanning curves for patch serialization. Fractal curves maintain high spatial proximity and adapt to different image resolutions, avoiding redundancy and enhancing SSMs' ability to model complex patterns accurately. We validate our method in image classification, detection, and segmentation tasks, and the superior performance validates its effectiveness.
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
Authors: Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, Romann M. Weber
Date: 2024-05-23 00:00:00
Tasks: Computational Efficiency, Decoder, GPU, Image Generation, SSIM

Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains underexplored. In this paper, we introduce LiteVAE, a new autoencoder design for LDMs, which leverages the 2D discrete wavelet transform to enhance scalability and computational efficiency over standard variational autoencoders (VAEs) with no sacrifice in output quality. We investigate the training methodologies and the decoder architecture of LiteVAE and propose several enhancements that improve the training dynamics and reconstruction quality. Our base LiteVAE model matches the quality of the established VAEs in current LDMs with a six-fold reduction in encoder parameters, leading to faster training and lower GPU memory requirements, while our larger model outperforms VAEs of comparable complexity across all evaluated metrics (rFID, LPIPS, PSNR, and SSIM).
TIGER: Text-Instructed 3D Gaussian Retrieval and Coherent Editing
Authors: Teng Xu, Jiamin Chen, Peng Chen, Youjia Zhang, Junqing Yu, Wei Yang
Date: 2024-05-23 00:00:00
Tasks: 3DGS, Retrieval

Editing objects within a scene is a critical functionality required across a broad spectrum of applications in computer vision and graphics. As 3D Gaussian Splatting (3DGS) emerges as a frontier in scene representation, the effective modification of 3D Gaussian scenes has become increasingly vital. This process entails accurately retrieve the target objects and subsequently performing modifications based on instructions. Though available in pieces, existing techniques mainly embed sparse semantics into Gaussians for retrieval, and rely on an iterative dataset update paradigm for editing, leading to over-smoothing or inconsistency issues. To this end, this paper proposes a systematic approach, namely TIGER, for coherent text-instructed 3D Gaussian retrieval and editing. In contrast to the top-down language grounding approach for 3D Gaussians, we adopt a bottom-up language aggregation strategy to generate a denser language embedded 3D Gaussians that supports open-vocabulary retrieval. To overcome the over-smoothing and inconsistency issues in editing, we propose a Coherent Score Distillation (CSD) that aggregates a 2D image editing diffusion model and a multi-view diffusion model for score distillation, producing multi-view consistent editing with much finer details. In various experiments, we demonstrate that our TIGER is able to accomplish more consistent and realistic edits than prior work.
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images
Authors: Jamie Burke, Justin Engelmann, Charlene Hamid, Diana Moukaddem, Dan Pugh, Neeraj Dhaun, Amos Storkey, Niall Strang, Stuart King, Tom MacGillivray, Miguel O. Bernabeu, Ian J. C. MacCormick
Date: 2024-05-23 00:00:00
Tasks: CPU, Data Augmentation

The choroid is a key vascular layer of the eye, supplying oxygen to the retinal photoreceptors. Non-invasive enhanced depth imaging optical coherence tomography (EDI-OCT) has recently improved access and visualisation of the choroid, making it an exciting frontier for discovering novel vascular biomarkers in ophthalmology and wider systemic health. However, current methods to measure the choroid often require use of multiple, independent semi-automatic and deep learning-based algorithms which are not made open-source. Previously, Choroidalyzer -- an open-source, fully automatic deep learning method trained on 5,600 OCT B-scans from 385 eyes -- was developed to fully segment and quantify the choroid in EDI-OCT images, thus addressing these issues. Using the same dataset, we propose a Robust, Resolution-agnostic and Efficient Attention-based network for CHoroid segmentation (REACH). REACHNet leverages multi-resolution training with domain-specific data augmentation to promote generalisation, and uses a lightweight architecture with resolution-agnostic self-attention which is not only faster than Choroidalyzer's previous network (4 images/s vs. 2.75 images/s on a standard laptop CPU), but has greater performance for segmenting the choroid region, vessels and fovea (Dice coefficient for region 0.9769 vs. 0.9749, vessels 0.8612 vs. 0.8192 and fovea 0.8243 vs. 0.3783) due to its improved hyperparameter configuration and model training pipeline. REACHNet can be used with Choroidalyzer as a drop-in replacement for the original model and will be made available upon publication.
DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation
Authors: Yitong Yang, Xinli Xu, Haigen Hu, Haixia Long, Qianwei Zhou, Qiu Guan
Date: 2024-05-23 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Despite the recent progress in medical image segmentation with scribble-based annotations, the segmentation results of most models are still not ro-bust and generalizable enough in open environments. Evidential deep learn-ing (EDL) has recently been proposed as a promising solution to model predictive uncertainty and improve the reliability of medical image segmen-tation. However directly applying EDL to scribble-supervised medical im-age segmentation faces a tradeoff between accuracy and reliability. To ad-dress the challenge, we propose a novel framework called Dual-Branch Evi-dential Deep Learning (DuEDL). Firstly, the decoder of the segmentation network is changed to two different branches, and the evidence of the two branches is fused to generate high-quality pseudo-labels. Then the frame-work applies partial evidence loss and two-branch consistent loss for joint training of the model to adapt to the scribble supervision learning. The pro-posed method was tested on two cardiac datasets: ACDC and MSCMRseg. The results show that our method significantly enhances the reliability and generalization ability of the model without sacrificing accuracy, outper-forming state-of-the-art baselines. The code is available at https://github.com/Gardnery/DuEDL.
Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures
Authors: Moises Diaz, Miguel A. Ferrer, Soodamani Ramalingam, Richard Guest
Date: 2024-05-23 00:00:00

In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.
Qubit-efficient Variational Quantum Algorithms for Image Segmentation
Authors: Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske, Matthias Klusch, Andreas Dengel
Date: 2024-05-23 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum algorithms (VQAs) for unsupervised image segmentation to partition images into separate semantic regions. Specifically, we formulate the task as a graph cut optimization problem and employ two established qubit-efficient VQAs, which we refer to as Parametric Gate Encoding (PGE) and Ancilla Basis Encoding (ABE), to find the optimal segmentation mask. In addition, we propose Adaptive Cost Encoding (ACE), a new approach that leverages the same circuit architecture as ABE but adopts a problem-dependent cost function. We benchmark PGE, ABE and ACE on synthetically generated images, focusing on quality and trainability. ACE shows consistently faster convergence in training the parameterized quantum circuits in comparison to PGE and ABE. Furthermore, we provide a theoretical analysis of the scalability of these approaches against the Quantum Approximate Optimization Algorithm (QAOA), showing a significant cutback in the quantum resources, especially in the number of qubits that logarithmically depends on the number of pixels. The results validate the strengths of ACE, while concurrently highlighting its inherent limitations and challenges. This paves way for further research in quantum-enhanced computer vision.
Capsule Network Projectors are Equivariant and Invariant Learners
Authors: Miles Everett, Aiden Durrant, Mingjun Zhong, Georgios Leontidis
Date: 2024-05-23 00:00:00
Tasks: Self-Supervised Learning

Learning invariant representations has been the longstanding approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in representations, yet do so with highly prescribed architectures. In this work, we propose an invariant-equivariant self-supervised architecture that employs Capsule Networks (CapsNets) which have been shown to capture equivariance with respect to novel viewpoints. We demonstrate that the use of CapsNets in equivariant self-supervised architectures achieves improved downstream performance on equivariant tasks with higher efficiency and fewer network parameters. To accommodate the architectural changes of CapsNets, we introduce a new objective function based on entropy minimisation. This approach which we name CapsIE (Capsule Invariant Equivariant Network) achieves state-of-the-art performance on the equivariant rotation tasks on the 3DIEBench dataset compared to prior equivariant SSL methods, while performing competitively against supervised counterparts. Our results demonstrate the ability of CapsNets to learn complex and generalised representations for large-scale, multi-task datasets compared to previous CapsNet benchmarks. Code is available at https://github.com/AberdeenML/CapsIE.
Efficient Visual State Space Model for Image Deblurring
Authors: Lingshun Kong, Jiangxin Dong, Ming-Hsuan Yang, Jinshan Pan
Date: 2024-05-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Deblurring, Image Deblurring, Image Restoration, model, State Space Models

Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. ViTs typically yield superior results in image restoration compared to CNNs due to their ability to capture long-range dependencies and input-dependent characteristics. However, the computational complexity of Transformer-based models grows quadratically with the image resolution, limiting their practical appeal in high-resolution image restoration tasks. In this paper, we propose a simple yet effective visual state space model (EVSSM) for image deblurring, leveraging the benefits of state space models (SSMs) to visual data. In contrast to existing methods that employ several fixed-direction scanning for feature extraction, which significantly increases the computational cost, we develop an efficient visual scan block that applies various geometric transformations before each SSM-based module, capturing useful non-local information and maintaining high efficiency. Extensive experimental results show that the proposed EVSSM performs favorably against state-of-the-art image deblurring methods on benchmark datasets and real-captured images.
RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian
Authors: Zhiheng Feng, Wenhua Wu, Tianchen Deng, Hesheng Wang
Date: 2024-05-23 00:00:00
Tasks: Autonomous Driving, Surface Reconstruction

Road surface reconstruction plays a crucial role in autonomous driving, which can be used for road lane perception and autolabeling. Recently, mesh-based road surface reconstruction algorithms have shown promising reconstruction results. However, these mesh-based methods suffer from slow speed and poor reconstruction quality. To address these limitations, we propose a novel large-scale road surface reconstruction approach with meshgrid Gaussian, named RoGs. Specifically, we model the road surface by placing Gaussian surfels in the vertices of a uniformly distributed square mesh, where each surfel stores color, semantic, and geometric information. This square mesh-based layout covers the entire road with fewer Gaussian surfels and reduces the overlap between Gaussian surfels during training. In addition, because the road surface has no thickness, 2D Gaussian surfel is more consistent with the physical reality of the road surface than 3D Gaussian sphere. Then, unlike previous initialization methods that rely on point clouds, we introduce a vehicle pose-based initialization method to initialize the height and rotation of the Gaussian surfel. Thanks to this meshgrid Gaussian modeling and pose-based initialization, our method achieves significant speedups while improving reconstruction quality. We obtain excellent results in reconstruction of road surfaces in a variety of challenging real-world scenes.