pwc-archive/papers-with-abstracts huggingface dataset:
A Wearable Gait Monitoring System for 17 Gait Parameters Based on Computer Vision
Authors: Jiangang Chen, Yung-Hong Sun, Kristen Pickett, Barbara King, Yu Hen Hu, Hongrui Jiang
Date: 2024-11-16 00:00:00
Tasks: Gait Identification

We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a Force Sensitive Resistor (FSR) affixed to the heel of the shoe, combined with a custom-designed algorithm, is utilized to measure temporal gait parameters. Through testing on multiple participants and comparison with the gait mat, the proposed gait monitoring system exhibited notable performance, with the accuracy of all measured gait parameters exceeding 93.61%. The system also demonstrated a low drift of 4.89% during long-distance walking. A gait identification task conducted on participants using a trained Transformer model achieved 95.7% accuracy on the dataset collected by the proposed system, demonstrating that our hardware has the potential to collect long-sequence gait data suitable for integration with current Large Language Models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements.
EVT: Efficient View Transformation for Multi-Modal 3D Object Detection
Authors: Yongjin Lee, Hyeon-Mun Jeong, Yurim Jeon, Sanghyun Kim
Date: 2024-11-16 00:00:00
Tasks: 3D Object Detection, Decoder, object-detection, Object Detection, Sensor Fusion

Multi-modal sensor fusion in Bird's Eye View (BEV) representation has become the leading approach for 3D object detection. However, existing methods often rely on depth estimators or transformer encoders to transform image features into BEV space, which reduces robustness or introduces significant computational overhead. Moreover, the insufficient geometric guidance in view transformation results in ray-directional misalignments, limiting the effectiveness of BEV representations. To address these challenges, we propose Efficient View Transformation (EVT), a novel 3D object detection framework that constructs a well-structured BEV representation, improving both accuracy and efficiency. Our approach focuses on two key aspects. First, Adaptive Sampling and Adaptive Projection (ASAP), which utilizes LiDAR guidance to generate 3D sampling points and adaptive kernels, enables more effective transformation of image features into BEV space and a refined BEV representation. Second, an improved query-based detection framework, incorporating group-wise mixed query selection and geometry-aware cross-attention, effectively captures both the common properties and the geometric structure of objects in the transformer decoder. On the nuScenes test set, EVT achieves state-of-the-art performance of 75.3\% NDS with real-time inference speed.
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image
Authors: Jiawen Li, Qiehe Sun, Renao Yan, Yizhi Wang, Yuqiu Fu, Yani Wei, Tian Guan, Huijuan Shi, Yonghonghe He, Anjia Han
Date: 2024-11-16 00:00:00
Tasks: Classification, Diagnostic, image-classification, Image Classification, Representation Learning, text similarity, whole slide images

With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the diversity of lesion types and the complex relationships between each other, these techniques still deserve further exploration in addressing advanced pathology tasks. To this end, we introduce the concept of hierarchical pathological image classification and propose a representation learning called PathTree. PathTree considers the multi-classification of diseases as a binary tree structure. Each category is represented as a professional pathological text description, which messages information with a tree-like encoder. The interactive text features are then used to guide the aggregation of hierarchical multiple representations. PathTree uses slide-text similarity to obtain probability scores and introduces two extra tree specific losses to further constrain the association between texts and slides. Through extensive experiments on three challenging hierarchical classification datasets: in-house cryosectioned lung tissue lesion identification, public prostate cancer grade assessment, and public breast cancer subtyping, our proposed PathTree is consistently competitive compared to the state-of-the-art methods and provides a new perspective on the deep learning-assisted solution for more complex WSI classification.
AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations
Authors: Jiawei Mao, Yu Yang, Xuesong Yin, Ling Shao, Hao Tang
Date: 2024-11-16 00:00:00
Tasks: All, Image Restoration

Image restoration models often face the simultaneous interaction of multiple degradations in real-world scenarios. Existing approaches typically handle single or composite degradations based on scene descriptors derived from text or image embeddings. However, due to the varying proportions of different degradations within an image, these scene descriptors may not accurately differentiate between degradations, leading to suboptimal restoration in practical applications. To address this issue, we propose a novel Transformer-based restoration framework, AllRestorer. In AllRestorer, we enable the model to adaptively consider all image impairments, thereby avoiding errors from scene descriptor misdirection. Specifically, we introduce an All-in-One Transformer Block (AiOTB), which adaptively removes all degradations present in a given image by modeling the relationships between all degradations and the image embedding in latent space. To accurately address different variations potentially present within the same type of degradation and minimize ambiguity, AiOTB utilizes a composite scene descriptor consisting of both image and text embeddings to define the degradation. Furthermore, AiOTB includes an adaptive weight for each degradation, allowing for precise control of the restoration intensity. By leveraging AiOTB, AllRestorer avoids misdirection caused by inaccurate scene descriptors, achieving a 5.00 dB increase in PSNR compared to the baseline on the CDD-11 dataset.
Poster: Reliable 3D Reconstruction for Ad-hoc Edge Implementations
Authors: Md Nurul Absur, Swastik Brahma, Saptarshi Debroy
Date: 2024-11-16 00:00:00
Tasks: 3D Reconstruction, Management, Multi-View 3D Reconstruction

Ad-hoc edge deployments to support real-time complex video processing applications such as, multi-view 3D reconstruction often suffer from spatio-temporal system disruptions that greatly impact reconstruction quality. In this poster paper, we present a novel portfolio theory-inspired edge resource management strategy to ensure reliable multi-view 3D reconstruction by accounting for possible system disruptions.
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
Authors: Ying Yang, De Cheng, Chaowei Fang, Yubiao Wang, Changzhe Jiao, Lechao Cheng, Nannan Wang
Date: 2024-11-16 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current reconstruction-based methods provide a good alternative approach by measuring the reconstruction error between the input and its corresponding generative counterpart in the pixel/feature space. However, such generative methods face a key dilemma: improving the reconstruction power of the generative model while keeping a compact representation of the ID data. To address this issue, we propose the diffusion-based layer-wise semantic reconstruction approach for unsupervised OOD detection. The innovation of our approach is that we leverage the diffusion model's intrinsic data reconstruction ability to distinguish ID samples from OOD samples in the latent feature space. Moreover, to set up a comprehensive and discriminative feature representation, we devise a multi-layer semantic feature extraction strategy. By distorting the extracted features with Gaussian noise and applying the diffusion model for feature reconstruction, the separation of ID and OOD samples is implemented according to the reconstruction errors. Extensive experimental results on multiple benchmarks built upon various datasets demonstrate that our method achieves state-of-the-art performance in terms of detection accuracy and speed. Code is available at <https://github.com/xbyym/DLSR>.
Multi-perspective Contrastive Logit Distillation
Authors: Qi Wang, Jinjia Zhou
Date: 2024-11-16 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Knowledge Distillation

We propose a novel and efficient logit distillation method, Multi-perspective Contrastive Logit Distillation (MCLD), which leverages contrastive learning to distill logits from multiple perspectives in knowledge distillation. Recent research on logit distillation has primarily focused on maximizing the information learned from the teacher model's logits to enhance the performance of the student model. To this end, we propose MCLD, which consists of three key components: Instance-wise CLD, Sample-wise CLD, and Category-wise CLD. These components are designed to facilitate the transfer of more information from the teacher's logits to the student model. Comprehensive evaluations on image classification tasks using CIFAR-100 and ImageNet, alongside representation transferability assessments on STL-10 and Tiny-ImageNet, highlight the significant advantages of our method. The knowledge distillation with our MCLD, surpasses existing state-of-the-art methods.
MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious Correlations
Authors: Qixuan Jin, Walter Gerych, Marzyeh Ghassemi
Date: 2024-11-16 00:00:00
Tasks: Denoising, Image Inpainting

Spurious features associated with class labels can lead image classifiers to rely on shortcuts that don't generalize well to new domains. This is especially problematic in medical settings, where biased models fail when applied to different hospitals or systems. In such cases, data-driven methods to reduce spurious correlations are preferred, as clinicians can directly validate the modified images. While Denoising Diffusion Probabilistic Models (Diffusion Models) show promise for natural images, they are impractical for medical use due to the difficulty of describing spurious medical features. To address this, we propose Masked Medical Image Inpainting (MaskMedPaint), which uses text-to-image diffusion models to augment training images by inpainting areas outside key classification regions to match the target domain. We demonstrate that MaskMedPaint enhances generalization to target domains across both natural (Waterbirds, iWildCam) and medical (ISIC 2018, Chest X-ray) datasets, given limited unlabeled target images.
From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling
Authors: Jinhong Lin, Cheng-En Wu, Huanran Li, Jifan Zhang, Yu Hen Hu, Pedro Morgado
Date: 2024-11-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Representation Learning, Self-Supervised Learning

Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations by predicting masked portions of images from their visible regions. While this approach has shown promising results, we hypothesize that its effectiveness may be limited by optimization challenges during early training stages, where models are expected to learn complex image distributions from partial observations before developing basic visual processing capabilities. To address this limitation, we propose a prototype-driven curriculum leagrning framework that structures the learning process to progress from prototypical examples to more complex variations in the dataset. Our approach introduces a temperature-based annealing scheme that gradually expands the training distribution, enabling more stable and efficient learning trajectories. Through extensive experiments on ImageNet-1K, we demonstrate that our curriculum learning strategy significantly improves both training efficiency and representation quality while requiring substantially fewer training epochs compared to standard Masked Auto-Encoding. Our findings suggest that carefully controlling the order of training examples plays a crucial role in self-supervised visual learning, providing a practical solution to the early-stage optimization challenges in MIM.
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
Authors: Haoxu Huang, Cem M. Deniz, Kyunghyun Cho, Sumit Chopra, Divyam Madaan
Date: 2024-11-16 00:00:00
Tasks: Decision Making, Diagnostic

Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these images, most overlook patients' historical data. To bridge this gap, we introduce Temporal MIMIC dataset, which integrates five years of patient history, including radiographic scans and reports from MIMIC-CXR and MIMIC-IV, encompassing 12,221 patients and thirteen pathologies. Building on this, we present HIST-AID, a framework that enhances automatic diagnostic accuracy using historical reports. HIST-AID emulates the radiologist's comprehensive approach, leveraging historical data to improve diagnostic accuracy. Our experiments demonstrate significant improvements, with AUROC increasing by 6.56% and AUPRC by 9.51% compared to models that rely solely on radiographic scans. These gains were consistently observed across diverse demographic groups, including variations in gender, age, and racial categories. We show that while recent data boost performance, older data may reduce accuracy due to changes in patient conditions. Our work paves the potential of incorporating historical data for more reliable automatic diagnosis, providing critical support for clinical decision-making.
Underwater Image Enhancement with Cascaded Contrastive Learning
Authors: Yi Liu, Qiuping Jiang, Xinyi Wang, Ting Luo, Jingchun Zhou
Date: 2024-11-16 00:00:00
Tasks: Contrastive Learning, Image Enhancement, UIE

Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. In this paper, we propose to address this issue by designing a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage. The proposed method is called CCL-Net in short. Specifically, the proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images. To guarantee the underwater image can be progressively enhanced, we also apply contrastive loss as an additional constraint to guide the training of each stage. In the first stage, the raw underwater images are used as negative samples for building the first contrastive loss, ensuring the enhanced results of the first color correction stage are better than the original inputs. While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results. Extensive experiments on multiple benchmark datasets demonstrate that our CCL-Net can achieve superior performance compared to many state-of-the-art methods. The source code of CCL-Net will be released at https://github.com/lewis081/CCL-Net.
SPDFusion: An Infrared and Visible Image Fusion Network Based on a Non-Euclidean Representation of Riemannian Manifolds
Authors: Huan Kang, Hui Li, Tianyang Xu, Rui Wang, Xiao-Jun Wu, Josef Kittler
Date: 2024-11-16 00:00:00
Tasks: Infrared And Visible Image Fusion, Representation Learning

Euclidean representation learning methods have achieved commendable results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually have a non-Euclidean structure, where Euclidean metric might be limited in representing the true data relationships, degrading fusion performance. To address this issue, a novel SPD (symmetric positive definite) manifold learning framework is proposed for multi-modal image fusion, named SPDFusion, which extends the image fusion approach from the Euclidean space to the SPD manifolds. Specifically, we encode images according to the Riemannian geometry to exploit their intrinsic statistical correlations, thereby aligning with human visual perception. Actually, the SPD matrix underpins our network learning, with a cross-modal fusion strategy employed to harness modality-specific dependencies and augment complementary information. Subsequently, an attention module is designed to process the learned weight matrix, facilitating the weighting of spatial global correlation semantics via SPD matrix multiplication. Based on this, we design an end-to-end fusion network based on cross-modal manifold learning. Extensive experiments on public datasets demonstrate that our framework exhibits superior performance compared to the current state-of-the-art methods.
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts
Authors: Jinqiang Long, Yanqi Dai, Guoxing Yang, Hongpeng Lin, Nanyi Fei, Yizhao Gao, Zhiwu Lu
Date: 2024-11-16 00:00:00
Tasks: Mixture-of-Experts, Optical Character Recognition (OCR), Visual Question Answering (VQA)

As the research of Multimodal Large Language Models (MLLMs) becomes popular, an advancing MLLM model is typically required to handle various textual and visual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for real-world applications. However, due to the significant differences in representation and distribution among data from various tasks, simply mixing data of all tasks together leads to the well-known``multi-task conflict" issue, resulting in performance degradation across various tasks. To address this issue, we propose Awaker2.5-VL, a Mixture of Experts~(MoE) architecture suitable for MLLM, which acquires the multi-task capabilities through multiple sparsely activated experts. To speed up the training and inference of Awaker2.5-VL, each expert in our model is devised as a low-rank adaptation (LoRA) structure. Extensive experiments on multiple latest benchmarks demonstrate the effectiveness of Awaker2.5-VL. The code and model weight are released in our Project Page: https://github.com/MetabrainAGI/Awaker.
Segmentation of Ink and Parchment in Dead Sea Scroll Fragments
Authors: Berat Kurar-Barakat, Nachum Dershowitz
Date: 2024-11-16 00:00:00
Tasks: Binarization, Segmentation

The discovery of the Dead Sea Scrolls over 60 years ago is widely regarded as one of the greatest archaeological breakthroughs in modern history. Recent study of the scrolls presents ongoing computational challenges, including determining the provenance of fragments, clustering fragments based on their degree of similarity, and pairing fragments that originate from the same manuscript -- all tasks that require focusing on individual letter and fragment shapes. This paper presents a computational method for segmenting ink and parchment regions in multispectral images of Dead Sea Scroll fragments. Using the newly developed Qumran Segmentation Dataset (QSD) consisting of 20 fragments, we apply multispectral thresholding to isolate ink and parchment regions based on their unique spectral signatures. To refine segmentation accuracy, we introduce an energy minimization technique that leverages ink contours, which are more distinguishable from the background and less noisy than inner ink regions. Experimental results demonstrate that this Multispectral Thresholding and Energy Minimization (MTEM) method achieves significant improvements over traditional binarization approaches like Otsu and Sauvola in parchment segmentation and is successful at delineating ink borders, in distinction from holes and background regions.
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts
Authors: Ayesha Siddiqua, Atib Mohammad Oni, Abu Saleh Musa Miah, Jungpil Shin
Date: 2024-11-16 00:00:00
Tasks: Data Augmentation

Post-traumatic stress disorder (PTSD) is a significant mental health challenge that affects individuals exposed to traumatic events. Early detection and effective intervention for PTSD are crucial, as it can lead to long-term psychological distress if untreated. Accurate detection of PTSD is essential for timely and targeted mental health interventions, especially in disaster-affected populations. Existing research has explored machine learning approaches for classifying PTSD, but many face limitations in terms of model performance and generalizability. To address these issues, we implemented a comprehensive preprocessing pipeline. This included data cleaning, missing value treatment using the SimpleImputer, label encoding of categorical variables, data augmentation using SMOTE to balance the dataset, and feature scaling with StandardScaler. The dataset was split into 80\% training and 20\% testing. We developed an ensemble model using a majority voting technique among several classifiers, including Logistic Regression, Support Vector Machines (SVM), Random Forest, XGBoost, LightGBM, and a customized Artificial Neural Network (ANN). The ensemble model achieved an accuracy of 96.76\% with a benchmark dataset, significantly outperforming individual models. The proposed method's advantages include improved robustness through the combination of multiple models, enhanced ability to generalize across diverse data points, and increased accuracy in detecting PTSD. Additionally, the use of SMOTE for data augmentation ensured better handling of imbalanced datasets, leading to more reliable predictions. The proposed approach offers valuable insights for policymakers and healthcare providers by leveraging predictive analytics to address mental health issues in vulnerable populations, particularly those affected by disasters.