pwc-archive/papers-with-abstracts huggingface dataset:
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks
Authors: Aagam Shah, Reimar Weissbach, David A. Griggs, A. John Hart, Elif Ertekin, Sameh Tawfick
Date: 2024-09-26 00:00:00
Tasks: Image Augmentation, Image Segmentation, Semantic Segmentation, Transfer Learning

With the increasing adoption of metal additive manufacturing (AM), researchers and practitioners are turning to data-driven approaches to optimise printing conditions. Cross-sectional images of melt tracks provide valuable information for tuning process parameters, developing parameter scaling data, and identifying defects. Here we present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 99% and an F1 score over 90%. The neural network exhibits robustness when tested on images captured by various users, printed on different machines, and acquired using different microscopes. A post-processing module extracts the height and width of the melt pool, and the wetting angles. We discuss opportunities to improve model performance and avenues for transfer learning, such as extension to other AM processes such as directed energy deposition.
Realistic Evaluation of Model Merging for Compositional Generalization
Authors: Derek Tam, Yash Kant, Brian Lester, Igor Gilitschenski, Colin Raffel
Date: 2024-09-26 00:00:00
Tasks: image-classification, Image Classification, Image Generation

Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance. This popularity has spurred rapid development of many new merging methods, which are typically validated in disparate experimental settings and frequently differ in the assumptions made about model architecture, data availability, and computational budget. In this work, we characterize the relative merits of different merging methods by evaluating them in a shared experimental setting and precisely identifying the practical requirements of each method. Specifically, our setting focuses on using merging for compositional generalization of capabilities in image classification, image generation, and natural language processing. Additionally, we measure the computational costs of different merging methods as well as how they perform when scaling the number of models being merged. Taken together, our results clarify the state of the field of model merging and provide a comprehensive and rigorous experimental setup to test new methods.
Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control
Authors: Xiaoyu Ji, Jan P Allebach, Ali Shakouri, Fengqing Zhu
Date: 2024-09-26 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suffers from inconsistency issues. Food crystal segmentation is a challenging problem due to the diverse shapes of crystals and their surrounding hard mimics. To address this challenge, we propose an efficient instance segmentation method based on object detection. Experimental results show that the predicted crystal counting accuracy of our method is comparable with existing segmentation methods, while being five times faster. Based on our experiments, we also define objective criteria for separating hard mimics and food crystals, which could benefit manual annotation tasks on similar dataset.
Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study
Authors: Qing Lyu, Jin Young Kim, Jeongchul Kim, Christopher T Whitlow
Date: 2024-09-26 00:00:00
Tasks: Image Generation

Beta-amyloid positron emission tomography (A$\beta$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. However, the high cost, limited availability, and exposure to radioactivity restrict the widespread use of A$\beta$-PET imaging, leading to a scarcity of comprehensive datasets. Previous studies have suggested that structural magnetic resonance imaging (MRI), which is more readily available, may serve as a viable alternative for synthesizing A$\beta$-PET images. In this study, we propose an approach to utilize 3D diffusion models to synthesize A$\beta$-PET images from T1-weighted MRI scans, aiming to overcome the limitations associated with direct PET imaging. Our method generates high-quality A$\beta$-PET images for cognitive normal cases, although it is less effective for mild cognitive impairment (MCI) patients due to the variability in A$\beta$ deposition patterns among subjects. Our preliminary results suggest that incorporating additional data, such as a larger sample of MCI cases and multi-modality information including clinical and demographic details, cognitive and functional assessments, and longitudinal data, may be necessary to improve A$\beta$-PET image synthesis for MCI patients.
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
Authors: Grzegorz Rypeść, Sebastian Cygert, Tomasz Trzciński, Bartłomiej Twardowski
Date: 2024-09-26 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Exemplar-Free, Incremental Learning

Exemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods represent classes as Gaussian distributions in the feature extractor's latent space, enabling Bayes classification or training the classifier by replaying pseudo features. However, we identify two critical issues that compromise their efficacy when the feature extractor is updated on incremental tasks. First, they do not consider that classes' covariance matrices change and must be adapted after each task. Second, they are susceptible to a task-recency bias caused by dimensionality collapse occurring during training. In this work, we propose AdaGauss -- a novel method that adapts covariance matrices from task to task and mitigates the task-recency bias owing to the additional anti-collapse loss function. AdaGauss yields state-of-the-art results on popular EFCIL benchmarks and datasets when training from scratch or starting from a pre-trained backbone. The code is available at: https://github.com/grypesc/AdaGauss.
Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification
Authors: Anirudh Mazumder, Jianguo Liu
Date: 2024-09-26 00:00:00
Tasks: Lung Disease Classification

Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.
Amodal Instance Segmentation with Diffusion Shape Prior Estimation
Authors: Minh Tran, Khoa Vo, Tri Nguyen, Ngan Le
Date: 2024-09-26 00:00:00
Tasks: Amodal Instance Segmentation, Instance Segmentation, Object, Prediction, Segmentation, Semantic Segmentation

Amodal Instance Segmentation (AIS) presents an intriguing challenge, including the segmentation prediction of both visible and occluded parts of objects within images. Previous methods have often relied on shape prior information gleaned from training data to enhance amodal segmentation. However, these approaches are susceptible to overfitting and disregard object category details. Recent advancements highlight the potential of conditioned diffusion models, pretrained on extensive datasets, to generate images from latent space. Drawing inspiration from this, we propose AISDiff with a Diffusion Shape Prior Estimation (DiffSP) module. AISDiff begins with the prediction of the visible segmentation mask and object category, alongside occlusion-aware processing through the prediction of occluding masks. Subsequently, these elements are inputted into our DiffSP module to infer the shape prior of the object. DiffSP utilizes conditioned diffusion models pretrained on extensive datasets to extract rich visual features for shape prior estimation. Additionally, we introduce the Shape Prior Amodal Predictor, which utilizes attention-based feature maps from the shape prior to refine amodal segmentation. Experiments across various AIS benchmarks demonstrate the effectiveness of our AISDiff.
Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming
Authors: Chen Li, Tongyu Zong, Yueyu Hu, Yao Wang, Yong liu
Date: 2024-09-26 00:00:00
Tasks: Decision Making, Prediction

Field-of-View (FoV) adaptive streaming significantly reduces bandwidth requirement of immersive point cloud video (PCV) by only transmitting visible points in a viewer's FoV. The traditional approaches often focus on trajectory-based 6 degree-of-freedom (6DoF) FoV predictions. The predicted FoV is then used to calculate point visibility. Such approaches do not explicitly consider video content's impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming. We reformulate the PCV FoV prediction problem from the cell visibility perspective, allowing for precise decision-making regarding the transmission of 3D data at the cell level based on the predicted visibility distribution. We develop a novel spatial visibility and object-aware graph model that leverages the historical 3D visibility data and incorporates spatial perception, neighboring cell correlation, and occlusion information to predict the cell visibility in the future. Our model significantly improves the long-term cell visibility prediction, reducing the prediction MSE loss by up to 50% compared to the state-of-the-art models while maintaining real-time performance (more than 30fps) for point cloud videos with over 1 million points.
Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks
Authors: Debargha Ganguly, Debayan Gupta, Vipin Chaudhary
Date: 2024-09-26 00:00:00

Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks often oversimplify, focusing on single-object tasks and not fully representing complex real-world anomalies. This paper introduces a new, straightforward method employing graph structures and topological features to effectively detect both far-OOD and near-OOD data. We convert images into networks of interconnected human understandable features or visual concepts. Through extensive testing on two novel tasks, including ablation studies with large vocabularies and diverse tasks, we demonstrate the method's effectiveness. This approach enhances DNN resilience to OOD data and promises improved performance in various applications.
Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions
Authors: Abhishek Jha, Tinne Tuytelaars
Date: 2024-09-26 00:00:00
Tasks: Representation Learning

In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastive), namely random crop and cutout. Our contributions are: (a) we dissociate random cropping into two separate augmentations, overlap and patch, and provide a detailed analysis on the effect of area of overlap and patch size to the accuracy on down stream tasks. (b) We offer an insight into why cutout augmentation does not learn good representation, as reported in earlier literature. Finally, based on these analysis, (c) we propose a distance-based margin to the invariance loss for learning scene-centric representations for the downstream task on object-centric distribution, showing that as simple as a margin proportional to the pixel distance between the two spatial views in the scence-centric images can improve the learned representation. Our study furthers the understanding of the spatial augmentations, and the effect of the domain-gap between the training augmentations and the test distribution.
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction
Authors: Jiayin Zhao, Zhifeng Zhao, Jiamin Wu, Tao Yu, Hui Qiao
Date: 2024-09-26 00:00:00
Tasks: Computational Efficiency

Light field microscopy (LFM) has been widely utilized in various fields for its capability to efficiently capture high-resolution 3D scenes. Despite the rapid advancements in neural representations, there are few methods specifically tailored for microscopic scenes. Existing approaches often do not adequately address issues such as the loss of high-frequency information due to defocus and sample aberration, resulting in suboptimal performance. In addition, existing methods, including RLD, INR, and supervised U-Net, face challenges such as sensitivity to initial estimates, reliance on extensive labeled data, and low computational efficiency, all of which significantly diminish the practicality in complex biological scenarios. This paper introduces PNR (Physics-informed Neural Representation), a method for high-resolution LFM reconstruction that significantly enhances performance. Our method incorporates an unsupervised and explicit feature representation approach, resulting in a 6.1 dB improvement in PSNR than RLD. Additionally, our method employs a frequency-based training loss, enabling better recovery of high-frequency details, which leads to a reduction in LPIPS by at least half compared to SOTA methods (1.762 V.S. 3.646 of DINER). Moreover, PNR integrates a physics-informed aberration correction strategy that optimizes Zernike polynomial parameters during optimization, thereby reducing the information loss caused by aberrations and improving spatial resolution. These advancements make PNR a promising solution for long-term high-resolution biological imaging applications. Our code and dataset will be made publicly available.
Learning to Drive via Asymmetric Self-Play
Authors: Chris Zhang, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang, Dian Chen, Sergio Casas, Raquel Urtasun
Date: 2024-09-26 00:00:00

Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of driving data is uninteresting, and deliberately collecting new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real data with additional challenging, solvable, and realistic synthetic scenarios. Our approach pairs a teacher that learns to generate scenarios it can solve but the student cannot, with a student that learns to solve them. When applied to traffic simulation, we learn realistic policies with significantly fewer collisions in both nominal and long-tail scenarios. Our policies further zero-shot transfer to generate training data for end-to-end autonomy, significantly outperforming state-of-the-art adversarial approaches, or using real data alone. For more information, visit https://waabi.ai/selfplay .
Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks
Authors: Vitaliy Kinakh, Brian Pulfer, Yury Belousov, Pierre Fernandez, Teddy Furon, Slava Voloshynovskiy
Date: 2024-09-26 00:00:00

The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these challenges. Its evolution spans three generations: handcrafted, autoencoder-based, and foundation model based methods. While the robustness of these systems is well-documented, the security against adversarial attacks remains underexplored. This paper evaluates the security of foundation models' latent space digital watermarking systems that utilize adversarial embedding techniques. A series of experiments investigate the security dimensions under copy and removal attacks, providing empirical insights into these systems' vulnerabilities. All experimental codes and results are available at https://github.com/vkinakh/ssl-watermarking-attacks .
Toward Efficient Deep Blind RAW Image Restoration
Authors: Marcos V. Conde, Florin Vasluianu, Radu Timofte
Date: 2024-09-26 00:00:00
Tasks: Deblurring, Denoising, Image Restoration, Super-Resolution

Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling the degradations in the sRGB domain is complicated because of the Image Signal Processor (ISP) transformations. Despite of this known issue, very few methods in the literature work directly with sensor RAW images. In this work we tackle image restoration directly in the RAW domain. We design a new realistic degradation pipeline for training deep blind RAW restoration models. Our pipeline considers realistic sensor noise, motion blur, camera shake, and other common degradations. The models trained with our pipeline and data from multiple sensors, can successfully reduce noise and blur, and recover details in RAW images captured from different cameras. To the best of our knowledge, this is the most exhaustive analysis on RAW image restoration. Code available at https://github.com/mv-lab/AISP
Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
Authors: Mankeerat Sidhu, Hetarth Chopra, Ansel Blume, Jeonghwan Kim, Revanth Gangi Reddy, Heng Ji
Date: 2024-09-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Retrieval, Object, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection, Retrieval

In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image-weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 48.7% mAP improvement on ODinW and 59.1% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.
MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction
Authors: Jingyu Song, Xudong Chen, Liupei Lu, Jie Li, Katherine A. Skinner
Date: 2024-09-26 00:00:00
Tasks: Autonomous Driving, Online Vectorized HD Map Construction

High-definition (HD) maps provide environmental information for autonomous driving systems and are essential for safe planning. While existing methods with single-frame input achieve impressive performance for online vectorized HD map construction, they still struggle with complex scenarios and occlusions. We propose MemFusionMap, a novel temporal fusion model with enhanced temporal reasoning capabilities for online HD map construction. Specifically, we contribute a working memory fusion module that improves the model's memory capacity to reason across a history of frames. We also design a novel temporal overlap heatmap to explicitly inform the model about the temporal overlap information and vehicle trajectory in the Bird's Eye View space. By integrating these two designs, MemFusionMap significantly outperforms existing methods while also maintaining a versatile design for scalability. We conduct extensive evaluation on open-source benchmarks and demonstrate a maximum improvement of 5.4% in mAP over state-of-the-art methods. The project page for MemFusionMap is https://song-jingyu.github.io/MemFusionMap
SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
Authors: Mengwen Ye, Yingzi Huangfu, You Li, Zekuan Yu
Date: 2024-09-25 00:00:00
Tasks: Clinical Knowledge, image-classification, Image Classification, Memorization

Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. To address this challenge, we propose a robust framework, Self-Supervised Pre-training with Robust Adaptive Credal Loss (SSP-RACL), for handling label noise in fundus image datasets. First, we use Masked Autoencoders (MAE) for pre-training to extract features, unaffected by label noise. Subsequently, RACL employ a superset learning framework, setting confidence thresholds and adaptive label relaxation parameter to construct possibility distributions and provide more reliable ground-truth estimates, thus effectively suppressing the memorization effect. Additionally, we introduce clinical knowledge-based asymmetric noise generation to simulate real-world noisy fundus image datasets. Experimental results demonstrate that our proposed method outperforms existing approaches in handling label noise, showing superior performance.
RepairBench: Leaderboard of Frontier Models for Program Repair
Authors: André Silva, Martin Monperrus
Date: 2024-09-27 00:00:00
Tasks: Program Repair

AI-driven program repair uses AI models to repair buggy software by producing patches. Rapid advancements in AI surely impact state-of-the-art performance of program repair. Yet, grasping this progress requires frequent and standardized evaluations. We propose RepairBench, a novel leaderboard for AI-driven program repair. The key characteristics of RepairBench are: 1) it is execution-based: all patches are compiled and executed against a test suite, 2) it assesses frontier models in a frequent and standardized way. RepairBench leverages two high-quality benchmarks, Defects4J and GitBug-Java, to evaluate frontier models against real-world program repair tasks. We publicly release the evaluation framework of RepairBench. We will update the leaderboard as new frontier models are released.