pwc-archive/papers-with-abstracts huggingface dataset:
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
Authors: Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles, Timothy Jackson
Date: 2024-03-22 00:00:00
Tasks: Domain Generalization, Test-time Adaptation

This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse clinical settings, our method leverages the rich information in the unlabelled multi-view imaging data to improve model robustness and accuracy. By incorporating a class balancing method, a test-time adaptation technique and a multi-view optimization strategy, we address the critical issue of domain shift that often hampers the performance of machine learning models in real-world applications. Experiments comparing various state-of-the-art domain generalization and test-time optimization methodologies show that our approach consistently outperforms when combined with existing baseline and state-of-the-art methods. We also show our online method improves all existing techniques. Our framework demonstrates improvements in domain generalization capabilities and offers a practical solution for real-world deployment by facilitating online adaptation to new, unseen datasets. Our code is available at https://github.com/zgy600/RetiGen .
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting
Authors: Jun Guo, Xiaojian Ma, Yue Fan, Huaping Liu, Qing Li
Date: 2024-03-22 00:00:00
Tasks: Instance Segmentation, Object Localization, Scene Understanding, Segmentation, Semantic Segmentation, Semantic Segmentation on ScanNet

Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, with wide-ranging applications in embodied agents and augmented reality systems. Existing methods adopt neurel rendering methods as 3D representations and jointly optimize color and semantic features to achieve rendering and scene understanding simultaneously. In this paper, we introduce Semantic Gaussians, a novel open-vocabulary scene understanding approach based on 3D Gaussian Splatting. Our key idea is to distill knowledge from 2D pre-trained models to 3D Gaussians. Unlike existing methods, we design a versatile projection approach that maps various 2D semantic features from pre-trained image encoders into a novel semantic component of 3D Gaussians, which is based on spatial relationship and need no additional training. We further build a 3D semantic network that directly predicts the semantic component from raw 3D Gaussians for fast inference. The quantitative results on ScanNet segmentation and LERF object localization demonstates the superior performance of our method. Additionally, we explore several applications of Semantic Gaussians including object part segmentation, instance segmentation, scene editing, and spatiotemporal segmentation with better qualitative results over 2D and 3D baselines, highlighting its versatility and effectiveness on supporting diverse downstream tasks.
InterFusion: Text-Driven Generation of 3D Human-Object Interaction
Authors: Sisi Dai, Wenhao Li, Haowen Sun, Haibin Huang, Chongyang Ma, Hui Huang, Kai Xu, Ruizhen Hu
Date: 2024-03-22 00:00:00
Tasks: 3D Generation, global-optimization, Human-Object Interaction Detection, Object, Text to 3D

In this study, we tackle the complex task of generating 3D human-object interactions (HOI) from textual descriptions in a zero-shot text-to-3D manner. We identify and address two key challenges: the unsatisfactory outcomes of direct text-to-3D methods in HOI, largely due to the lack of paired text-interaction data, and the inherent difficulties in simultaneously generating multiple concepts with complex spatial relationships. To effectively address these issues, we present InterFusion, a two-stage framework specifically designed for HOI generation. InterFusion involves human pose estimations derived from text as geometric priors, which simplifies the text-to-3D conversion process and introduces additional constraints for accurate object generation. At the first stage, InterFusion extracts 3D human poses from a synthesized image dataset depicting a wide range of interactions, subsequently mapping these poses to interaction descriptions. The second stage of InterFusion capitalizes on the latest developments in text-to-3D generation, enabling the production of realistic and high-quality 3D HOI scenes. This is achieved through a local-global optimization process, where the generation of human body and object is optimized separately, and jointly refined with a global optimization of the entire scene, ensuring a seamless and contextually coherent integration. Our experimental results affirm that InterFusion significantly outperforms existing state-of-the-art methods in 3D HOI generation.
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Authors: Cosmin Ciausu, Deepa Krishnaswamy, Benjamin Billot, Steve Pieper, Ron Kikinis, Andrey Fedorov
Date: 2024-03-22 00:00:00
Tasks: Computed Tomography (CT), Organ Segmentation, Segmentation

Deep learning has shown great promise in the ability to automatically annotate organs in magnetic resonance imaging (MRI) scans, for example, of the brain. However, despite advancements in the field, the ability to accurately segment abdominal organs remains difficult across MR. In part, this may be explained by the much greater variability in image appearance and severely limited availability of training labels. The inherent nature of computed tomography (CT) scans makes it easier to annotate, resulting in a larger availability of expert annotations for the latter. We leverage a modality-agnostic domain randomization approach, utilizing CT label maps to generate synthetic images on-the-fly during training, further used to train a U-Net segmentation network for abdominal organs segmentation. Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data. Our method results in Dice scores of 0.90 (0.08) and 0.91 (0.08) for the right and left kidney respectively, compared to a pretrained nnU-Net model yielding 0.87 (0.20) and 0.91 (0.03). We will make our code publicly available.
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Authors: Khiem Le, Long Ho, Cuong Do, Danh Le-Phuoc, Kok-Seng Wong
Date: 2024-03-22 00:00:00
Proceeding: CVPR 2024 1
Tasks: Domain Generalization, Federated Learning, Privacy Preserving

Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce significant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN, which relies on a normalization scheme working with a guiding regularizer. In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client models selectively filtering domain-specific features that are biased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effective regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in addressing this particular problem.
FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs
Authors: Sepehr Dehdashtian, Lan Wang, Vishnu Naresh Boddeti
Date: 2024-03-22 00:00:00
Tasks: Fairness

Large pre-trained vision-language models such as CLIP provide compact and general-purpose representations of text and images that are demonstrably effective across multiple downstream zero-shot prediction tasks. However, owing to the nature of their training process, these models have the potential to 1) propagate or amplify societal biases in the training data and 2) learn to rely on spurious features. This paper proposes FairerCLIP, a general approach for making zero-shot predictions of CLIP more fair and robust to spurious correlations. We formulate the problem of jointly debiasing CLIP's image and text representations in reproducing kernel Hilbert spaces (RKHSs), which affords multiple benefits: 1) Flexibility: Unlike existing approaches, which are specialized to either learn with or without ground-truth labels, FairerCLIP is adaptable to learning in both scenarios. 2) Ease of Optimization: FairerCLIP lends itself to an iterative optimization involving closed-form solvers, which leads to $4\times$-$10\times$ faster training than the existing methods. 3) Sample Efficiency: Under sample-limited conditions, FairerCLIP significantly outperforms baselines when they fail entirely. And, 4) Performance: Empirically, FairerCLIP achieves appreciable accuracy gains on benchmark fairness and spurious correlation datasets over their respective baselines.
U-ARE-ME: Uncertainty-Aware Rotation Estimation in Manhattan Environments
Authors: Aalok Patwardhan, Callum Rhodes, Gwangbin Bae, Andrew J. Davison
Date: 2024-03-22 00:00:00

Camera rotation estimation from a single image is a challenging task, often requiring depth data and/or camera intrinsics, which are generally not available for in-the-wild videos. Although external sensors such as inertial measurement units (IMUs) can help, they often suffer from drift and are not applicable in non-inertial reference frames. We present U-ARE-ME, an algorithm that estimates camera rotation along with uncertainty from uncalibrated RGB images. Using a Manhattan World assumption, our method leverages the per-pixel geometric priors encoded in single-image surface normal predictions and performs optimisation over the SO(3) manifold. Given a sequence of images, we can use the per-frame rotation estimates and their uncertainty to perform multi-frame optimisation, achieving robustness and temporal consistency. Our experiments demonstrate that U-ARE-ME performs comparably to RGB-D methods and is more robust than sparse feature-based SLAM methods. We encourage the reader to view the accompanying video at https://callum-rhodes.github.io/U-ARE-ME for a visual overview of our method.
Augmented Reality Warnings in Roadway Work Zones: Evaluating the Effect of Modality on Worker Reaction Times
Authors: Sepehr Sabeti, Fatemeh Banani Ardecani, Omidreza Shoghli
Date: 2024-03-22 00:00:00
Tasks: Pose Estimation

Given the aging highway infrastructure requiring extensive rebuilding and enhancements, and the consequent rise in the number of work zones, there is an urgent need to develop advanced safety systems to protect workers. While Augmented Reality (AR) holds significant potential for delivering warnings to workers, its integration into roadway work zones remains relatively unexplored. The primary objective of this study is to improve safety measures within roadway work zones by conducting an extensive analysis of how different combinations of multimodal AR warnings influence the reaction times of workers. This paper addresses this gap through a series of experiments that aim to replicate the distinctive conditions of roadway work zones, both in real-world and virtual reality environments. Our approach comprises three key components: an advanced AR system prototype, a VR simulation of AR functionality within the work zone environment, and the Wizard of Oz technique to synchronize user experiences across experiments. To assess reaction times, we leverage both the simple reaction time (SRT) technique and an innovative vision-based metric that utilizes real-time pose estimation. By conducting five experiments in controlled outdoor work zones and indoor VR settings, our study provides valuable information on how various multimodal AR warnings impact workers reaction times. Furthermore, our findings reveal the disparities in reaction times between VR simulations and real-world scenarios, thereby gauging VR's capability to mirror the dynamics of roadway work zones. Furthermore, our results substantiate the potential and reliability of vision-based reaction time measurements. These insights resonate well with those derived using the SRT technique, underscoring the viability of this approach for tangible real-world uses.
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Authors: Shambhavi Mishra, Balamurali Murugesan, Ismail Ben Ayed, Marco Pedersoli, Jose Dolz
Date: 2024-03-22 00:00:00
Tasks: image-classification, Image Classification

State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the training on unlabeled samples. An inherent drawback of this strategy stems from the quality of the uncertainty estimates, as pseudo-labels are filtered only based on their degree of uncertainty, regardless of the correctness of their predictions. Thus, assessing and enhancing the uncertainty of network predictions is of paramount importance in the pseudo-labeling process. In this work, we empirically demonstrate that SSL methods based on pseudo-labels are significantly miscalibrated, and formally demonstrate the minimization of the min-entropy, a lower bound of the Shannon entropy, as a potential cause for miscalibration. To alleviate this issue, we integrate a simple penalty term, which enforces the logit distances of the predictions on unlabeled samples to remain low, preventing the network predictions to become overconfident. Comprehensive experiments on a variety of SSL image classification benchmarks demonstrate that the proposed solution systematically improves the calibration performance of relevant SSL models, while also enhancing their discriminative power, being an appealing addition to tackle SSL tasks.
A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation
Authors: Kyle Lucke, Aleksandar Vakanski, Min Xian
Date: 2024-03-22 00:00:00
Tasks: Anatomy, Semantic Segmentation

In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) Most current approaches inherently lack the ability to utilize tissue anatomy, resulting in misclassified image regions. 2) They struggle to produce accurate boundaries due to the repeated down-sampling operations. To address these issues, we propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. Extensive experiments are conducted to compare the proposed method and eight state-of-the-art approaches using a BUS dataset with 325 images. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes and produces more accurate fine details of tissue boundaries.
Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting
Authors: Zheng Zhang, WenBo Hu, Yixing Lao, Tong He, Hengshuang Zhao
Date: 2024-03-22 00:00:00
Tasks: 3DGS, NeRF, Novel View Synthesis

3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance. However, it relies heavily on the quality of the initial point cloud, resulting in blurring and needle-like artifacts in areas with insufficient initializing points. This is mainly attributed to the point cloud growth condition in 3DGS that only considers the average gradient magnitude of points from observable views, thereby failing to grow for large Gaussians that are observable for many viewpoints while many of them are only covered in the boundaries. To this end, we propose a novel method, named Pixel-GS, to take into account the number of pixels covered by the Gaussian in each view during the computation of the growth condition. We regard the covered pixel numbers as the weights to dynamically average the gradients from different views, such that the growth of large Gaussians can be prompted. As a result, points within the areas with insufficient initializing points can be grown more effectively, leading to a more accurate and detailed reconstruction. In addition, we propose a simple yet effective strategy to scale the gradient field according to the distance to the camera, to suppress the growth of floaters near the camera. Extensive experiments both qualitatively and quantitatively demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time rendering speed, on the challenging Mip-NeRF 360 and Tanks & Temples datasets.
Medical Image Data Provenance for Medical Cyber-Physical System
Authors: Vijay Kumar, Kolin Paul
Date: 2024-03-22 00:00:00
Tasks: Computational Efficiency, Diagnostic

Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile applications. To mitigate these risks, this study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images, ensuring data provenance. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage, thus enabling verification of image authenticity and source. Moreover, a robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems. Through a case study on mobile fundus imaging, the effectiveness of the proposed framework is evaluated in terms of computational efficiency, image quality, security, and trustworthiness. This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
Authors: Jaeill Kim, Wonseok Lee, Moonjung Eo, Wonjong Rhee
Date: 2024-03-22 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studies have predominantly focused on backward compatible approaches to mitigate catastrophic forgetting, recent investigations have introduced forward compatible methods to enhance performance on novel tasks and complement existing backward compatible methods. In this study, we introduce an effective-Rank based Feature Richness enhancement (RFR) method, designed for improving forward compatibility. Specifically, this method increases the effective rank of representations during the base session, thereby facilitating the incorporation of more informative features pertinent to unseen novel tasks. Consequently, RFR achieves dual objectives in backward and forward compatibility: minimizing feature extractor modifications and enhancing novel task performance, respectively. To validate the efficacy of our approach, we establish a theoretical connection between effective rank and the Shannon entropy of representations. Subsequently, we conduct comprehensive experiments by integrating RFR into eleven well-known CIL methods. Our results demonstrate the effectiveness of our approach in enhancing novel-task performance while mitigating catastrophic forgetting. Furthermore, our method notably improves the average incremental accuracy across all eleven cases examined.
On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques
Authors: Laura O'Mahony, David JP O'Sullivan, Nikola S. Nikolov
Date: 2024-03-21 00:00:00

Out-of-distribution data and anomalous inputs are vulnerabilities of machine learning systems today, often causing systems to make incorrect predictions. The diverse range of data on which these models are used makes detecting atypical inputs a difficult and important task. We assess a tool, Benford's law, as a method used to quantify the difference between real and corrupted inputs. We believe that in many settings, it could function as a filter for anomalous data points and for signalling out-of-distribution data. We hope to open a discussion on these applications and further areas where this technique is underexplored.
A note on generalization bounds for losses with finite moments
Authors: Borja Rodríguez-Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund
Date: 2024-03-25 00:00:00
Tasks: Generalization Bounds

This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the $p$-th moment is bounded, the resulting bounds interpolate between a slow rate $1 / \sqrt{n}$ when $p=2$, and a fast rate $1 / n$ when $p \to \infty$ and the loss is essentially bounded. Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance. This bound has an exponentially better dependence on the confidence parameter and the dependency measure than previous bounds in the literature. Finally, the paper extends all results to guarantees in expectation and single-draw PAC-Bayes. In order to so, it obtains analogues of the PAC-Bayes fast rate bound for bounded losses from [2] in these settings.
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
Authors: Rene Winchenbach, Nils Thuerey
Date: 2024-03-25 00:00:00
Tasks: Physical Simulations

Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics. Classic numerical solvers have traditionally been computationally expensive and challenging to use in inverse problems, whereas Neural solvers aim to address both concerns through machine learning. We propose a general formulation for continuous convolutions using separable basis functions as a superset of existing methods and evaluate a large set of basis functions in the context of (a) a compressible 1D SPH simulation, (b) a weakly compressible 2D SPH simulation, and (c) an incompressible 2D SPH Simulation. We demonstrate that even and odd symmetries included in the basis functions are key aspects of stability and accuracy. Our broad evaluation shows that Fourier-based continuous convolutions outperform all other architectures regarding accuracy and generalization. Finally, using these Fourier-based networks, we show that prior inductive biases, such as window functions, are no longer necessary. An implementation of our approach, as well as complete datasets and solver implementations, is available at https://github.com/tum-pbs/SFBC.