pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging
Authors: Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau
Date: 2023-10-07 00:00:00

Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for MR classification called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of the heart. It uses standard computer vision techniques and contrastive models to learn from large amounts of unlabeled data, in conjunction with specialized classifiers to establish the first ever automated MR classification system. Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences, CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the first benchmark result for this new task.
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations
Authors: Yue Jiang, Eldon Schoop, Amanda Swearngin, Jeffrey Nichols
Date: 2023-10-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Multimodal Vision-Language Models (VLMs) enable powerful applications from their fused understanding of images and language, but many perform poorly on UI tasks due to the lack of UI training data. In this paper, we adapt a recipe for generating paired text-image training data for VLMs to the UI domain by combining existing pixel-based methods with a Large Language Model (LLM). Unlike prior art, our method requires no human-provided annotations, and it can be applied to any dataset of UI screenshots. We generate a dataset of 335K conversational examples paired with UIs that cover Q&A, UI descriptions, and planning, and use it to fine-tune a conversational VLM for UI tasks. To assess the performance of our model, we benchmark it on UI element detection tasks, evaluate response quality, and showcase its applicability to multi-step UI navigation and planning.
End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition
Authors: Emilian-Claudiu Mănescu, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop
Date: 2023-10-07 00:00:00
Tasks: Domain Adaptation, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and optimizations on the underrepresented, short-scale Romanian language dataset called Wild LRRo. Most notably, we compare different backend modules, demonstrating the effectiveness of adding ample regularization methods. We obtain state-of-the-art results using our proposed method, namely cross-lingual domain adaptation and unlabeled videos from English and German datasets to help the model learn language-invariant features. Lastly, we assess the performance of adding a layer inspired by the neural inhibition mechanism.
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
Authors: Chenhao Lin, Fangbin Yi, Hang Wang, Qian Li, Deng Jingyi, Chao Shen
Date: 2023-10-07 00:00:00

Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge. However, existing methods predominantly concentrate on single-face manipulation detection, leaving the more intricate and realistic realm of multi-face forgeries relatively unexplored. This paper proposes a novel framework explicitly tailored for multi-face forgery detection,filling a critical gap in the current research. The framework mainly involves two modules:(i) a facial relationships learning module, which generates distinguishable local features for each face within images,(ii) a global feature aggregation module that leverages the mutual constraints between global and local information to enhance forgery detection accuracy.Our experimental results on two publicly available multi-face forgery datasets demonstrate that the proposed approach achieves state-of-the-art performance in multi-face forgery detection scenarios.
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles
Authors: Elton F. de S. Soares, Carlos Alberto V. Campos
Date: 2023-10-07 00:00:00
Tasks: Autonomous Vehicles, Depth Estimation, Federated Learning, Monocular Depth Estimation, Pose Estimation, Self-Supervised Learning, Semantic Segmentation

Image-based depth estimation has gained significant attention in recent research on computer vision for autonomous vehicles in intelligent transportation systems. This focus stems from its cost-effectiveness and wide range of potential applications. Unlike binocular depth estimation methods that require two fixed cameras, monocular depth estimation methods only rely on a single camera, making them highly versatile. While state-of-the-art approaches for this task leverage self-supervised learning of deep neural networks in conjunction with tasks like pose estimation and semantic segmentation, none of them have explored the combination of federated learning and self-supervision to train models using unlabeled and private data captured by autonomous vehicles. The utilization of federated learning offers notable benefits, including enhanced privacy protection, reduced network consumption, and improved resilience to connectivity issues. To address this gap, we propose FedSCDepth, a novel method that combines federated learning and deep self-supervision to enable the learning of monocular depth estimators with comparable effectiveness and superior efficiency compared to the current state-of-the-art methods. Our evaluation experiments conducted on Eigen's Split of the KITTI dataset demonstrate that our proposed method achieves near state-of-the-art performance, with a test loss below 0.13 and requiring, on average, only 1.5k training steps and up to 0.415 GB of weight data transfer per autonomous vehicle on each round.
Extract-Transform-Load for Video Streams
Authors: Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, Samuel Madden
Date: 2023-10-07 00:00:00
Tasks: Self-Driving Cars

Social media, self-driving cars, and traffic cameras produce video streams at large scales and cheap cost. However, storing and querying video at such scales is prohibitively expensive. We propose to treat large-scale video analytics as a data warehousing problem: Video is a format that is easy to produce but needs to be transformed into an application-specific format that is easy to query. Analogously, we define the problem of Video Extract-Transform-Load (V-ETL). V-ETL systems need to reduce the cost of running a user-defined V-ETL job while also giving throughput guarantees to keep up with the rate at which data is produced. We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL. Skyscraper can execute arbitrary video ingestion pipelines and adaptively tunes them to reduce cost at minimal or no quality degradation, e.g., by adjusting sampling rates and resolutions to the ingested content. Skyscraper can hereby be provisioned with cheap on-premises compute and uses a combination of buffering and cloud bursting to deal with peaks in workload caused by expensive processing configurations. In our experiments, we find that Skyscraper significantly reduces the cost of V-ETL ingestion compared to adaptions of current SOTA systems, while at the same time giving robustness guarantees that these systems are lacking.
How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty
Authors: Denis Mbey Akola, Gianni Franchi
Date: 2023-10-07 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

This paper presents a new approach for training two-stage object detection ensemble models, more specifically, Faster R-CNN models to estimate uncertainty. We propose training one Region Proposal Network(RPN) and multiple Fast R-CNN prediction heads is all you need to build a robust deep ensemble network for estimating uncertainty in object detection. We present this approach and provide experiments to show that this approach is much faster than the naive method of fully training all $n$ models in an ensemble. We also estimate the uncertainty by measuring this ensemble model's Expected Calibration Error (ECE). We then further compare the performance of this model with that of Gaussian YOLOv3, a variant of YOLOv3 that models uncertainty using predicted bounding box coordinates. The source code is released at \url{https://github.com/Akola-Mbey-Denis/EfficientEnsemble}
Comparative study of multi-person tracking methods
Authors: Denis Mbey Akola
Date: 2023-10-07 00:00:00
Tasks: Pedestrian Detection

This paper presents a study of two tracking algorithms (SORT~\cite{7533003} and Tracktor++~\cite{2019}) that were ranked first positions on the MOT Challenge leaderboard (The MOTChallenge web page: https://motchallenge.net ). The purpose of this study is to discover the techniques used and to provide useful insights about these algorithms in the tracking pipeline that could improve the performance of MOT tracking algorithms. To this end, we adopted the popular tracking-by-detection approach. We trained our own Pedestrian Detection model using the MOT17Det dataset (MOT17Det : https://motchallenge.net/data/MOT17Det/ ). We also used a re-identification model trained on MOT17 dataset (MOT17 : https://motchallenge.net/data/MOT17/ ) for Tracktor++ to reduce the false re-identification alarms. We then present experimental results which shows that Tracktor++ is a better multi-person tracking algorithm than SORT. We also performed ablation studies to discover the contribution of re-identification(RE-ID) network and motion to the results of Tracktor++. We finally conclude by providing some recommendations for future research.
Hacking Generative Models with Differentiable Network Bending
Authors: Giacomo Aldegheri, Alina Rogalska, Ahmed Youssef, Eugenia Iofinova
Date: 2023-10-07 00:00:00

In this work, we propose a method to 'hack' generative models, pushing their outputs away from the original training distribution towards a new objective. We inject a small-scale trainable module between the intermediate layers of the model and train it for a low number of iterations, keeping the rest of the network frozen. The resulting output images display an uncanny quality, given by the tension between the original and new objectives that can be exploited for artistic purposes.
Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global Warming
Authors: Zhenkuan Wang
Date: 2023-10-07 00:00:00
Tasks: Autonomous Driving, Model Selection, Semantic Segmentation

Semantic segmentation is a critical tool in computer vision, applied in various domains like autonomous driving and medical imaging. This study focuses on aircraft contrail detection in global satellite images to improve contrail models and mitigate their impact on climate change.An innovative data preprocessing technique for NOAA GOES-16 satellite images is developed, using brightness temperature data from the infrared channel to create false-color images, enhancing model perception. To tackle class imbalance, the training dataset exclusively includes images with positive contrail labels.The model selection is based on the UPerNet architecture, implemented using the MMsegmentation library, with the integration of two ConvNeXt configurations for improved performance. Cross-entropy loss with positive class weights enhances contrail recognition. Fine-tuning employs the AdamW optimizer with a learning rate of $2.5 \times 10^{-4}$.During inference, a multi-model prediction fusion strategy and a contrail determination threshold of 0.75 yield a binary prediction mask. RLE encoding is used for efficient prediction result organization.The approach achieves exceptional results, boasting a high Dice coefficient score, placing it in the top 5\% of participating teams. This underscores the innovative nature of the segmentation model and its potential for enhanced contrail recognition in satellite imagery.For further exploration, the code and models are available on GitHub: \url{https://github.com/biluko/2023GRIC.git}.
Towards Long-Range 3D Object Detection for Autonomous Vehicles
Authors: Ajinkya Khoche, Laura Pereira Sánchez, Nazre Batool, Sina Sharif Mansouri, Patric Jensfelt
Date: 2023-10-07 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Depth Completion, Depth Estimation, Object, object-detection, Object Detection, Self-Driving Cars

3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long range. To address the above limitations, we investigate two ways to improve long range performance of current LiDAR based 3D detectors. First, we combine two 3D detection networks, referred to as range experts, one specializing at near to mid range objects, and one at long range 3D detection. To train a detector at long range under a scarce label regime, we further weigh the loss according to the labelled point's distance from ego vehicle. Second, we augment LiDAR scans with virtual points generated using Multimodal Virtual Points (MVP), a readily available image-based depth completion algorithm. Our experiments on the long range Argoverse2 (AV2) dataset indicate that MVP is more effective in improving long range performance, while maintaining a straightforward implementation. On the other hand, the range experts offer a computationally efficient and simpler alternative, avoiding dependency on image-based segmentation networks and perfect camera-LiDAR calibration.
HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields
Authors: Wei zhang, Tiecheng Sun, Sen Wang, Qing Cheng, Norbert Haala
Date: 2023-10-07 00:00:00
Tasks: global-optimization, Simultaneous Localization and Mapping

In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. To address these limitations, our approach integrates dense-SLAM with neural implicit fields. Specifically, our dense SLAM approach runs parallel tracking and global optimization, while a neural field-based map is constructed incrementally based on the latest SLAM estimates. For the efficient construction of neural fields, we employ multi-resolution grid encoding and signed distance function (SDF) representation. This allows us to keep the map always up-to-date and adapt instantly to global updates via loop closing. For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift. To enhance depth accuracy further, we incorporate learned monocular depth priors. We propose a novel joint depth and scale adjustment (JDSA) module to solve the scale ambiguity inherent in depth priors. Extensive evaluations across synthetic and real-world datasets validate that our approach outperforms existing methods in accuracy and map completeness while preserving real-time performance.
TransCC: Transformer Network for Coronary Artery CCTA Segmentation
Authors: Chenchu Xu, Meng Li, Xue Wu
Date: 2023-10-07 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The accurate segmentation of Coronary Computed Tomography Angiography (CCTA) images holds substantial clinical value for the early detection and treatment of Coronary Heart Disease (CHD). The Transformer, utilizing a self-attention mechanism, has demonstrated commendable performance in the realm of medical image processing. However, challenges persist in coronary segmentation tasks due to (1) the damage to target local structures caused by fixed-size image patch embedding, and (2) the critical role of both global and local features in medical image segmentation tasks.To address these challenges, we propose a deep learning framework, TransCC, that effectively amalgamates the Transformer and convolutional neural networks for CCTA segmentation. Firstly, we introduce a Feature Interaction Extraction (FIE) module designed to capture the characteristics of image patches, thereby circumventing the loss of semantic information inherent in the original method. Secondly, we devise a Multilayer Enhanced Perceptron (MEP) to augment attention to local information within spatial dimensions, serving as a complement to the self-attention mechanism. Experimental results indicate that TransCC outperforms existing methods in segmentation performance, boasting an average Dice coefficient of 0.730 and an average Intersection over Union (IoU) of 0.582. These results underscore the effectiveness of TransCC in CCTA image segmentation.
1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction
Authors: Zhishan Zhou, Zhi Lv, Shihao Zhou, Minqiang Zou, Tong Wu, Mochen Yu, Yao Tang, Jiajun Liang
Date: 2023-10-07 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

This report introduce our work on Egocentric 3D Hand Pose Estimation workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand pose estimation from a single-view image. In the competition, we adopt ViT based backbones and a simple regressor for 3D keypoints prediction, which provides strong model baselines. We noticed that Hand-objects occlusions and self-occlusions lead to performance degradation, thus proposed a non-model method to merge multi-view results in the post-process stage. Moreover, We utilized test time augmentation and model ensemble to make further improvement. We also found that public dataset and rational preprocess are beneficial. Our method achieved 12.21mm MPJPE on test dataset, achieve the first place in Egocentric 3D Hand Pose Estimation challenge.
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification
Authors: Dennis Ritter, Mike Hemberger, Marc Hönig, Volker Stopp, Erik Rodner, Kristian Hildebrand
Date: 2023-10-07 00:00:00
Tasks: Domain Adaptation, Object, Transfer Learning, Unsupervised Domain Adaptation

In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting. In contrast to standard natural object benchmarks existing in the field, our results highlight the most important design choices when only category-labeled CAD models are available but classification needs to be done with real-world images. Our domain adaptation pipeline achieves SoTA performance on the VisDA benchmark, but more importantly, drastically improves recognition performance on our new open industrial dataset comprised of 102 mechanical parts. We conclude with a set of guidelines that are relevant for practitioners needing to apply state-of-the-art unsupervised domain adaptation in practice. Our code is available at https://github.com/dritter-bht/synthnet-transfer-learning.
A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset
Authors: Andreas Voskou, Konstantinos P. Panousis, Harris Partaourides, Kyriakos Tolias, Sotirios Chatzis
Date: 2023-10-07 00:00:00
Tasks: Sign Language Translation, Translation

Automatic Sign Language Translation (SLT) is a research avenue of great societal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing (HoH) with hearing people, thus improving their social life and opportunities for participation in social life. However, research within this frame of reference is still in its infancy, and current resources are particularly limited. Existing SLT methods are either of low translation ability or are trained and evaluated on datasets of restricted vocabulary and questionable real-world value. A characteristic example is Phoenix2014T benchmark dataset, which only covers weather forecasts in German Sign Language. To address this shortage of resources, we introduce a newly constructed collection of 29653 Greek Sign Language video-translation pairs which is based on the official syllabus of Greek Elementary School. Our dataset covers a wide range of subjects. We use this novel dataset to train recent state-of-the-art Transformer-based methods widely used in SLT research. Our results demonstrate the potential of our introduced dataset to advance SLT research by offering a favourable balance between usability and real-world value.
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
Authors: Wenhao Li, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu
Date: 2023-10-07 00:00:00
Tasks: Denoising

Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior synthetic performance. In this paper, we meticulously analyze the diffusion model and engineer a base model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Moreover, we employ RFID as a proxy to promptly rank the experimental outcomes produced by GPT-4. We also adopt a rapid-convergence training strategy to boost search efficiency. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37 improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm.
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Authors: Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Yash Raj Shrestha, Binod Bhattarai
Date: 2023-10-07 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images. To overcome this challenge, we employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for instance segmentation tasks. Our empirical findings affirm that the proposed model exhibits commendable performance in identifying stenotic lesions. Notably, our approach achieves a substantial F1 score of 0.5353 in this demanding task, underscoring its effectiveness in streamlining this intensive process.