pwc-archive/papers-with-abstracts huggingface dataset:
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
Authors: Daniel Bogdoll, Iramm Hamdard, Lukas Namgyu Rößler, Felix Geisler, Muhammed Bayram, Felix Wang, Jan Imhof, Miguel de Campos, Anushervon Tabarov, Yitian Yang, Hanno Gottschalk, J. Marius Zöllner
Date: 2024-05-13 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Autonomous Vehicles

The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong focus on camera data. In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date. AnoVox incorporates large-scale multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. We propose a formal definition of normality and provide a compliant training dataset. AnoVox is the first benchmark to contain both content and temporal anomalies.
Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
Authors: Chi-en Amy Tai, Alexander Wong
Date: 2024-05-13 00:00:00

Breast cancer was diagnosed for over 7.8 million women between 2015 to 2020. Grading plays a vital role in breast cancer treatment planning. However, the current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. A recent paper leveraging volumetric deep radiomic features from synthetic correlated diffusion imaging (CDI$^s$) for breast cancer grade prediction showed immense promise for noninvasive methods for grading. Motivated by the impact of CDI$^s$ optimization for prostate cancer delineation, this paper examines using optimized CDI$^s$ to improve breast cancer grade prediction. We fuse the optimized CDI$^s$ signal with diffusion-weighted imaging (DWI) to create a multiparametric MRI for each patient. Using a larger patient cohort and training across all the layers of a pretrained MONAI model, we achieve a leave-one-out cross-validation accuracy of 95.79%, over 8% higher compared to that previously reported.
Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction
Authors: Chi-en Amy Tai, Alexander Wong
Date: 2024-05-13 00:00:00

In 2020, 685,000 deaths across the world were attributed to breast cancer, underscoring the critical need for innovative and effective breast cancer treatment. Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer, attributed to its efficacy in shrinking large tumors and leading to pathologic complete response. However, the current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts which contain inherent biases and significant uncertainty. A recent study, utilizing volumetric deep radiomic features extracted from synthetic correlated diffusion imaging (CDI$^s$), demonstrated significant potential in noninvasive breast cancer pathologic complete response prediction. Inspired by the positive outcomes of optimizing CDI$^s$ for prostate cancer delineation, this research investigates the application of optimized CDI$^s$ to enhance breast cancer pathologic complete response prediction. Using multiparametric MRI that fuses optimized CDI$^s$ with diffusion-weighted imaging (DWI), we obtain a leave-one-out cross-validation accuracy of 93.28%, over 5.5% higher than that previously reported.
SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling
Authors: Yijun Yuan, Michael Bleier, Andreas Nüchter
Date: 2024-05-13 00:00:00
Tasks: Depth Estimation

We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that conveniently supports a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RGB-D/RGB-L/Mono/Depth-only reconstruction and SLAM. The workflow-centric design uses multiple blocks as the basis for constructing different production lines. The supported applications, i.e., productions avoid redundancy in their designs. Thus, the focus is placed on each block itself for independent expansion. To support all input combinations, our implementation consists of four building blocks that form SceneFactory: (1) tracking, (2) flexion, (3) depth estimation, and (4) scene reconstruction. The tracking block is based on Mono SLAM and is extended to support RGB-D and RGB-LiDAR (RGB-L) inputs. Flexion is used to convert the depth image (untrackable) into a trackable image. For general-purpose depth estimation, we propose an unposed \& uncalibrated multi-view depth estimation model (U$^2$-MVD) to estimate dense geometry. U$^2$-MVD exploits dense bundle adjustment to solve for poses, intrinsics, and inverse depth. A semantic-aware ScaleCov step is then introduced to complete the multi-view depth. Relying on U$^2$-MVD, SceneFactory both supports user-friendly 3D creation (with just images) and bridges the applications of Dense RGB-D and Dense Mono. For high-quality surface and color reconstruction, we propose Dual-purpose Multi-resolutional Neural Points (DM-NPs) for the first surface accessible Surface Color Field design, where we introduce Improved Point Rasterization (IPR) for point cloud based surface query. ...
Multi-Task Learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network
Authors: Shulei QU, Zhenguo Gao, Xiaowei Chen, Na Li, Yakai Wang, Xiaoxiao Wu
Date: 2024-05-13 00:00:00
Tasks: Face Recognition, Multi-Task Learning

In driving scenarios, automobile active safety systems are increasingly incorporating deep learning technology. These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving and recognizing the driver's identity. However, the traditional parallel-style approach of combining multiple single-task models tends to waste resources when dealing with similar tasks. Therefore, we propose a novel tree-style multi-task modeling approach for multi-task learning, which rooted at a shared backbone, more dedicated separate module branches are appended as the model pipeline goes deeper. Following the tree-style approach, we propose a multi-task learning model for simultaneously performing driver fatigue detection and face recognition for identifying a driver. This model shares a common feature extraction backbone module, with further separated feature extraction and classification module branches. The dedicated branches exploit and combine spatial and channel attention mechanisms to generate space-channel fused-attention enhanced features, leading to improved detection performance. As only single-task datasets are available, we introduce techniques including alternating updation and gradient accumulation for training our multi-task model using only the single-task datasets. The effectiveness of our tree-style multi-task learning model is verified through extensive validations.
Ground-based image deconvolution with Swin Transformer UNet
Authors: Utsav Akhaury, Pascale Jablonka, Jean-Luc Starck, Frédéric Courbin
Date: 2024-05-13 00:00:00
Tasks: Computational Efficiency, Image Deconvolution

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.
NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images
Authors: Matthew Keller, Chi-en Amy Tai, Yuhao Chen, Pengcheng Xi, Alexander Wong
Date: 2024-05-13 00:00:00
Tasks: Nutrition

Many aging individuals encounter challenges in effectively tracking their dietary intake, exacerbating their susceptibility to nutrition-related health complications. Self-reporting methods are often inaccurate and suffer from substantial bias; however, leveraging intelligent prediction methods can automate and enhance precision in this process. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. Still, these methods are often tailored to specific situations, require other inputs in addition to a food image, or do not provide comprehensive nutritional information. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures to directly predict a meal's nutritional content from its image. Through comprehensive experimentation and evaluation, we present NutritionVerse-Direct, a model utilizing a vision transformer base architecture with three fully connected layers that lead to five regression heads predicting calories (kcal), mass (g), protein (g), fat (g), and carbohydrates (g) present in a meal. NutritionVerse-Direct yields a combined mean average error score on the NutritionVerse-Real dataset of 412.6, an improvement of 25.5% over the Inception-ResNet model, demonstrating its potential for improving dietary intake estimation accuracy.
Localizing Task Information for Improved Model Merging and Compression
Authors: Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez, François Fleuret, Pascal Frossard
Date: 2024-05-13 00:00:00
Tasks: Task Arithmetic

Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduced by significant performance loss. Previous works have linked these drops to interference in the weight space and erasure of important task-specific features. Instead, in this work we show that the information required to solve each task is still preserved after merging as different tasks mostly use non-overlapping sets of weights. We propose TALL-masks, a method to identify these task supports given a collection of task vectors and show that one can retrieve >99% of the single task accuracy by applying our masks to the multi-task vector, effectively compressing the individual checkpoints. We study the statistics of intersections among constructed masks and reveal the existence of selfish and catastrophic weights, i.e., parameters that are important exclusively to one task and irrelevant to all tasks but detrimental to multi-task fusion. For this reason, we propose Consensus Merging, an algorithm that eliminates such weights and improves the general performance of existing model merging approaches. Our experiments in vision and NLP benchmarks with up to 20 tasks, show that Consensus Merging consistently improves existing approaches. Furthermore, our proposed compression scheme reduces storage from 57Gb to 8.2Gb while retaining 99.7% of original performance.
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Authors: Jian Liu, Wei Sun, Hui Yang, Zhiwen Zeng, Chongpei Liu, Jin Zheng, Xingyu Liu, Hossein Rahmani, Nicu Sebe, Ajmal Mian
Date: 2024-05-13 00:00:00
Tasks: Deep Learning, Object, Pose Estimation, Survey

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, \emph{i.e.}, instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at https://github.com/CNJianLiu/Awesome-Object-Pose-Estimation.
Generating Human Motion in 3D Scenes from Text Descriptions
Authors: Zhi Cen, Huaijin Pi, Sida Peng, Zehong Shen, Minghui Yang, Shuai Zhu, Hujun Bao, Xiaowei Zhou
Date: 2024-05-13 00:00:00
Proceeding: CVPR 2024 1
Tasks: Motion Generation, Object, Spatial Reasoning

Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However, only a few works consider human-scene interactions together with text conditions, which is crucial for visual and physical realism. This paper focuses on the task of generating human motions in 3D indoor scenes given text descriptions of the human-scene interactions. This task presents challenges due to the multi-modality nature of text, scene, and motion, as well as the need for spatial reasoning. To address these challenges, we propose a new approach that decomposes the complex problem into two more manageable sub-problems: (1) language grounding of the target object and (2) object-centric motion generation. For language grounding of the target object, we leverage the power of large language models. For motion generation, we design an object-centric scene representation for the generative model to focus on the target object, thereby reducing the scene complexity and facilitating the modeling of the relationship between human motions and the object. Experiments demonstrate the better motion quality of our approach compared to baselines and validate our design choices.
GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images
Authors: Xinying Wang, Zhixiong Huang, Sifan Zhang, Jiawen Zhu, Paolo Gamba, Lin Feng
Date: 2024-05-13 00:00:00
Tasks: Computational Efficiency, Mamba, Spectral Reconstruction

Mainstream approaches to spectral reconstruction (SR) primarily focus on designing Convolution- and Transformer-based architectures. However, CNN methods often face challenges in handling long-range dependencies, whereas Transformers are constrained by computational efficiency limitations. Recent breakthroughs in state-space model (e.g., Mamba) has attracted significant attention due to its near-linear computational efficiency and superior performance, prompting our investigation into its potential for SR problem. To this end, we propose the Gradient-guided Mamba for Spectral Reconstruction from RGB Images, dubbed GMSR-Net. GMSR-Net is a lightweight model characterized by a global receptive field and linear computational complexity. Its core comprises multiple stacked Gradient Mamba (GM) blocks, each featuring a tri-branch structure. In addition to benefiting from efficient global feature representation by Mamba block, we further innovatively introduce spatial gradient attention and spectral gradient attention to guide the reconstruction of spatial and spectral cues. GMSR-Net demonstrates a significant accuracy-efficiency trade-off, achieving state-of-the-art performance while markedly reducing the number of parameters and computational burdens. Compared to existing approaches, GMSR-Net slashes parameters and FLOPS by substantial margins of 10 times and 20 times, respectively. Code is available at https://github.com/wxy11-27/GMSR.
SAR Image Synthesis with Diffusion Models
Authors: Denisa Qosja, Simon Wagner, Daniel O'Hagan
Date: 2024-05-13 00:00:00
Tasks: Denoising, Image Generation

In recent years, diffusion models (DMs) have become a popular method for generating synthetic data. By achieving samples of higher quality, they quickly became superior to generative adversarial networks (GANs) and the current state-of-the-art method in generative modeling. However, their potential has not yet been exploited in radar, where the lack of available training data is a long-standing problem. In this work, a specific type of DMs, namely denoising diffusion probabilistic model (DDPM) is adapted to the SAR domain. We investigate the network choice and specific diffusion parameters for conditional and unconditional SAR image generation. In our experiments, we show that DDPM qualitatively and quantitatively outperforms state-of-the-art GAN-based methods for SAR image generation. Finally, we show that DDPM profits from pretraining on largescale clutter data, generating SAR images of even higher quality.
A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms
Authors: Johan Öfverstedt, Elin Lundström, Göran Bergström, Joel Kullberg, Håkan Ahlström
Date: 2024-05-13 00:00:00
Tasks: Image Registration, Image Segmentation, Semantic Segmentation

The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging. In this work we have conducted a supervoxel-wise association study between both volumetric and tissue density features in coronary computed tomography angiograms and the chronological age of a subject, to understand the localized changes in morphology and tissue density with age. To enable a supervoxel-wise study of volume and tissue density, we developed a novel method based on image segmentation, inter-subject image registration, and robust supervoxel-based correlation analysis, to achieve a statistical association study between the images and age. We evaluate the registration methodology in terms of the Dice coefficient for the heart chambers and myocardium, and the inverse consistency of the transformations, showing that the method works well in most cases with high overlap and inverse consistency. In a sex-stratified study conducted on a subset of $n=1388$ images from the SCAPIS study, the supervoxel-wise analysis was able to find localized associations with age outside of the commonly segmented and analyzed sub-regions, and several substantial differences between the sexes in the association of age and volume.
Coarse or Fine? Recognising Action End States without Labels
Authors: Davide Moltisanti, Hakan Bilen, Laura Sevilla-Lara, Frank Keller
Date: 2024-05-13 00:00:00
Tasks: Action Recognition, Object

We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No dataset with these annotated end states is available, so we propose an augmentation method to synthesise training data. We apply this method to cutting actions extracted from an existing action recognition dataset. Our method is object agnostic, i.e., it presupposes the location of the object but not its identity. Starting from less than a hundred images of a whole object, we can generate several thousands images simulating visually diverse cuts of different coarseness. We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects. Results demonstrate that the model successfully recognises the end state of the cutting action despite the domain gap between training and testing, and that the model generalises well to unseen objects.
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
Authors: Liangrui Pan, Yijun Peng, Yan Li, Yiyi Liang, Liwen Xu, Qingchun Liang, Shaoliang Peng
Date: 2024-05-13 00:00:00
Tasks: Denoising, Prognosis, Representation Learning, Triplet

Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival. However, most existing methods ignore the simultaneous utilization of rich semantic features at different scales in pathology images. When collecting multimodal data and extracting features, there is a likelihood of encountering intra-modality missing data, introducing noise into the multimodal data. To address these challenges, this paper proposes a new end-to-end framework, FORESEE, for robustly predicting patient survival by mining multimodal information. Specifically, the cross-fusion transformer effectively utilizes features at the cellular level, tissue level, and tumor heterogeneity level to correlate prognosis through a cross-scale feature cross-fusion method. This enhances the ability of pathological image feature representation. Secondly, the hybrid attention encoder (HAE) uses the denoising contextual attention module to obtain the contextual relationship features and local detail features of the molecular data. HAE's channel attention module obtains global features of molecular data. Furthermore, to address the issue of missing information within modalities, we propose an asymmetrically masked triplet masked autoencoder to reconstruct lost information within modalities. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods on four benchmark datasets in both complete and missing settings.