pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data
Authors: Johannes Kopp, Dominik Kellner, Aldi Piroli, Vinzenz Dallabetta, Klaus Dietmayer
Date: 2023-11-13 00:00:00
Tasks: Autonomous Vehicles, Semantic Segmentation

The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter, i.e. erroneous points that do not correspond to real objects. Another common objective is the semantic segmentation of moving road users. These two problems are handled strictly separate from each other in literature. The employed neural networks are always focused entirely on only one of the tasks. In contrast to this, we examine ways to solve both tasks at the same time with a single jointly used model. In addition to a new augmented multi-head architecture, we also devise a method to represent a network's predictions for the two tasks with only one output value. This novel approach allows us to solve the tasks simultaneously with the same inference time as a conventional task-specific model. In an extensive evaluation, we show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset.
DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery
Authors: Yitong Sun, Zijian Zhou, Cyriel Diels, Ali Asadipour
Date: 2023-11-13 00:00:00
Tasks: Depth Estimation

Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask. Recent VR headsets are increasingly equipped with eye-oriented monocular cameras to segment ocular feature maps. Yet, to compute the incident light stimulus and observe periocular condition alterations, it is imperative to transform these relative measurements into metric dimensions. To bridge this gap, we propose a lightweight framework derived from the U-Net 3+ deep learning backbone that we re-optimised, to estimate measurable periocular depth maps. Compatible with any VR headset equipped with an eye-oriented monocular camera, our method reconstructs three-dimensional periocular regions, providing a metric basis for related light stimulus calculation protocols and medical guidelines. Navigating the complexities of data collection, we introduce a Dynamic Periocular Data Generation (DPDG) environment based on UE MetaHuman, which synthesises thousands of training images from a small quantity of human facial scan data. Evaluated on a sample of 36 participants, our method exhibited notable efficacy in the periocular global precision evaluation experiment, and the pupil diameter measurement.
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Authors: Paula Ramirez, Alena Uus, Milou P. M. van Poppel, Irina Grigorescu, Johannes K. Steinweg, David F. A. Lloyd, Kuberan Pushparajah, Andrew P. King, Maria Deprez
Date: 2023-11-13 00:00:00
Tasks: Anomaly Classification, Diagnostic, Multi-Task Learning, Segmentation, Weakly supervised segmentation

Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
Authors: Marc Aubreville, Zhaoya Pan, Matti Sievert, Jonas Ammeling, Jonathan Ganz, Nicolai Oetter, Florian Stelzle, Ann-Kathrin Frenken, Katharina Breininger, Miguel Goncalves
Date: 2023-11-13 00:00:00
Tasks: Few-Shot Learning

The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.
A method for quantifying sectoral optic disc pallor in fundus photographs and its association with peripapillary RNFL thickness
Authors: Samuel Gibbon, Graciela Muniz-Terrera, Fabian SL Yii, Charlene Hamid, Simon Cox, Ian JC Maccormick, Andrew J Tatham, Craig Ritchie, Emanuele Trucco, Baljean Dhillon, Thomas J MacGillivray
Date: 2023-11-13 00:00:00

Purpose: To develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fibre layer (pRNFL) thickness. Methods: We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants. Separately, we used images diagnosed by clinical inspection as pale (N=45) and assessed how measurements compared to healthy controls (N=46). We also developed automatic rejection thresholds, and tested the software for robustness to camera type, image format, and resolution. Results: We developed software that automatically quantified disc pallor across several zones in fundus photographs. Pallor was associated with pRNFL thickness globally (\b{eta} = -9.81 (SE = 3.16), p < 0.05), in the temporal inferior zone (\b{eta} = -29.78 (SE = 8.32), p < 0.01), with the nasal/temporal ratio (\b{eta} = 0.88 (SE = 0.34), p < 0.05), and in the whole disc (\b{eta} = -8.22 (SE = 2.92), p < 0.05). Furthermore, pallor was significantly higher in the patient group. Lastly, we demonstrate the analysis to be robust to camera type, image format, and resolution. Conclusions: We developed software that automatically locates and quantifies disc pallor in fundus photographs and found associations between pallor measurements and pRNFL thickness. Translational relevance: We think our method will be useful for the identification, monitoring and progression of diseases characterized by disc pallor/optic atrophy, including glaucoma, compression, and potentially in neurodegenerative disorders.
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
Authors: Shuwei Shao, Zhongcai Pei, Weihai Chen, Dingchi Sun, Peter C. Y. Chen, Zhengguo Li
Date: 2023-11-13 00:00:00
Tasks: Denoising, Depth Estimation, Monocular Depth Estimation

Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, e.g., occlusion and dynamic objects. In this work, we introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process. Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually adds noise to the depth map generated by a pre-trained teacher model. Moreover,the teacher model allows applying a distillation loss to guide the denoised depth. Further, we develop a masked visual condition mechanism to enhance the denoising ability of model. Extensive experiments are conducted on the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior state-of-the-art competitors. The source code will be available at https://github.com/ShuweiShao/MonoDiffusion.
Fitting tree model with CNN and geodesics to track vesselsand application to Ultrasound Localization Microscopy data
Authors: Théo Bertrand, Laurent D. Cohen
Date: 2023-11-13 00:00:00

Segmentation of tubular structures in vascular imaging is a well studied task, although it is rare that we try to infuse knowledge of the tree-like structure of the regions to be detected. Our work focuses on detecting the important landmarks in the vascular network (via CNN performing both localization and classification of the points of interest) and representing vessels as the edges in some minimal distance tree graph. We leverage geodesic methods relevant to the detection of vessels and their geometry, making use of the space of positions and orientations so that 2D vessels can be accurately represented as trees. We build our model to carry tracking on Ultrasound Localization Microscopy (ULM) data, proposing to build a good cost function for tracking on this type of data. We also test our framework on synthetic and eye fundus data. Results show that scarcity of well annotated ULM data is an obstacle to localization of vascular landmarks but the Orientation Score built from ULM data yields good geodesics for tracking blood vessels.
Regenerating Arbitrary Video Sequences with Distillation Path-Finding
Authors: Thi-Ngoc-Hanh Le, Sheng-Yi Yao, Chun-Te Wu, Tong-Yee Lee
Date: 2023-11-13 00:00:00
Tasks: Feature Correlation

If the video has long been mentioned as a widespread visualization form, the animation sequence in the video is mentioned as storytelling for people. Producing an animation requires intensive human labor from skilled professional artists to obtain plausible animation in both content and motion direction, incredibly for animations with complex content, multiple moving objects, and dense movement. This paper presents an interactive framework to generate new sequences according to the users' preference on the starting frame. The critical contrast of our approach versus prior work and existing commercial applications is that novel sequences with arbitrary starting frame are produced by our system with a consistent degree in both content and motion direction. To achieve this effectively, we first learn the feature correlation on the frameset of the given video through a proposed network called RSFNet. Then, we develop a novel path-finding algorithm, SDPF, which formulates the knowledge of motion directions of the source video to estimate the smooth and plausible sequences. The extensive experiments show that our framework can produce new animations on the cartoon and natural scenes and advance prior works and commercial applications to enable users to obtain more predictable results.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
Authors: Shuwei Shao, Zhongcai Pei, Weihai Chen, Peter C. Y. Chen, Zhengguo Li
Date: 2023-11-13 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Over the past few years, monocular depth estimation and completion have been paid more and more attention from the computer vision community because of their widespread applications. In this paper, we introduce novel physics (geometry)-driven deep learning frameworks for these two tasks by assuming that 3D scenes are constituted with piece-wise planes. Instead of directly estimating the depth map or completing the sparse depth map, we propose to estimate the surface normal and plane-to-origin distance maps or complete the sparse surface normal and distance maps as intermediate outputs. To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance. Meanwhile, the surface normal and distance maps are regularized by a developed plane-aware consistency constraint, which are then transformed into depth maps. Furthermore, we integrate an additional depth head to strengthen the robustness of the proposed frameworks. Extensive experiments on the NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds in performance prior state-of-the-art monocular depth estimation and completion competitors. The source code will be available at https://github.com/ShuweiShao/NDDepth.
CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
Authors: Taegun An, Changhee Joo
Date: 2023-11-13 00:00:00
Tasks: Image-to-Image Translation, Neural Architecture Search, Translation

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully address the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures.
Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
Authors: JunJie Huang, Yun Ye, Zhujin Liang, Yi Shan, Dalong Du
Date: 2023-11-13 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection

3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules. We refer to the data annotation in dataset construction for theory complementing and argue that the regression task prediction should not involve the feature from the camera branch. By following the cutting-edge perspective of 'Detecting As Labeling', we propose a novel paradigm dubbed DAL. With the most classical elementary algorithms, a simple predicting pipeline is constructed by imitating the data annotation process. Then we train it in the simplest way to minimize its dependency and strengthen its portability. Though simple in construction and training, the proposed DAL paradigm not only substantially pushes the performance boundary but also provides a superior trade-off between speed and accuracy among all existing methods. With comprehensive superiority, DAL is an ideal baseline for both future work development and practical deployment. The code has been released to facilitate future work on https://github.com/HuangJunJie2017/BEVDet.
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
Authors: Yunlong Zhang, Honglin Li, Yuxuan Sun, Sunyi Zheng, Chenglu Zhu, Lin Yang
Date: 2023-11-13 00:00:00
Tasks: image-classification, Image Classification, Multiple Instance Learning

In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting. To mitigate overfitting, we present Attention-Challenging MIL (ACMIL). ACMIL combines two techniques based on separate analyses for attention value concentration. Firstly, UMAP of instance features reveals various patterns among discriminative instances, with existing attention mechanisms capturing only some of them. To remedy this, we introduce Multiple Branch Attention (MBA) to capture more discriminative instances using multiple attention branches. Secondly, the examination of the cumulative value of Top-K attention scores indicates that a tiny number of instances dominate the majority of attention. In response, we present Stochastic Top-K Instance Masking (STKIM), which masks out a portion of instances with Top-K attention values and allocates their attention values to the remaining instances. The extensive experimental results on three WSI datasets with two pre-trained backbones reveal that our ACMIL outperforms state-of-the-art methods. Additionally, through heatmap visualization and UMAP visualization, this paper extensively illustrates ACMIL's effectiveness in suppressing attention value concentration and overcoming the overfitting challenge. The source code is available at \url{https://github.com/dazhangyu123/ACMIL}.
SpectralGPT: Spectral Remote Sensing Foundation Model
Authors: Danfeng Hong, Bing Zhang, Xuyang Li, YuXuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot
Date: 2023-11-13 00:00:00
Tasks: Change Detection, model, Representation Learning, Scene Classification, Scene Understanding, Semantic Segmentation

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings
Authors: Yachun Mi, Yu Li, Yan Shu, Chen Hui, Puchao Zhou, Shaohui Liu
Date: 2023-11-13 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Video Quality Assessment (VQA) aims to simulate the process of perceiving video quality by the human visual system (HVS). The judgments made by HVS are always influenced by human subjective feelings. However, most of the current VQA research focuses on capturing various distortions in the spatial and temporal domains of videos, while ignoring the impact of human feelings. In this paper, we propose CLiF-VQA, which considers both features related to human feelings and spatial features of videos. In order to effectively extract features related to human feelings from videos, we explore the consistency between CLIP and human feelings in video perception for the first time. Specifically, we design multiple objective and subjective descriptions closely related to human feelings as prompts. Further we propose a novel CLIP-based semantic feature extractor (SFE) which extracts features related to human feelings by sliding over multiple regions of the video frame. In addition, we further capture the low-level-aware features of the video through a spatial feature extraction module. The two different features are then aggregated thereby obtaining the quality score of the video. Extensive experiments show that the proposed CLiF-VQA exhibits excellent performance on several VQA datasets.
GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning
Authors: Qinlin He, Chunlei Peng, Decheng Liu, Nannan Wang, Xinbo Gao
Date: 2023-11-13 00:00:00
Tasks: DeepFake Detection, Face Swapping, Gaze Estimation

DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by scholars to incorporate biometric features into the field of DeepFake detection. However, traditional biometric-based approaches tend to segregate biometric features from general ones and freeze the biometric feature extractor. These approaches resulted in the exclusion of valuable general features, potentially leading to a performance decline and, consequently, a failure to fully exploit the potential of biometric information in assisting DeepFake detection. Moreover, insufficient attention has been dedicated to scrutinizing gaze authenticity within the realm of DeepFake detection in recent years. In this paper, we introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model to regularize the corresponding representation within our DeepFake detection model, while concurrently integrating general features to further enhance the performance of our model. Experiment results reveal that our proposed GazeForensics outperforms the current state-of-the-art methods.