pwc-archive/papers-with-abstracts huggingface dataset:
Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence
Authors: Pedro A. Moreno-Sanchez
Date: 2021-08-20 00:00:00
Tasks: Explainable artificial intelligence, feature selection, Prediction

Cardiovascular diseases and their associated disorder of heart failure are one of the major death causes globally, being a priority for doctors to detect and predict its onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnosis and treatments. Specifically, explainable AI offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of a prediction model for heart failure survival by using a dataset that comprises 299 patients who suffered heart failure. The model employs a data workflow pipeline able to select the best ensemble tree algorithm as well as the best feature selection technique. Moreover, different post-hoc techniques have been used for the explainability analysis of the model. The paper's main contribution is an explainability-driven approach to select the best prediction model for HF survival based on an accuracy-explainability balance. Therefore, the most balanced explainable prediction model implements an Extra Trees classifier over 5 selected features (follow-up time, serum creatinine, ejection fraction, age and diabetes) out of 12, achieving a balanced-accuracy of 85.1% and 79.5% with cross-validation and new unseen data respectively. The follow-up time is the most influencing feature followed by serum-creatinine and ejection-fraction. The explainable prediction model for HF survival presented in this paper would improve a further adoption of clinical prediction models by providing doctors with intuitions to better understand the reasoning of, usually, black-box AI clinical solutions, and make more reasonable and data-driven decisions.
Does Adversarial Oversampling Help us?
Authors: Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass
Date: 2021-08-20 00:00:00
Tasks: Robust classification

Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer an end-to-end solution. To overcome this, we propose a three-player adversarial game-based end-to-end method, where a domain-constraints mixture of generators, a discriminator, and a multi-class classifier are used. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. In AO, the generator updates by fooling both the classifier and discriminator, however, in DO, it updates by favoring the classifier and fooling the discriminator. While updating the classifier, it considers both the real and synthetically generated samples in AO. But, in DO, it favors the real samples and fools the subset class-specific generated samples. To mitigate the biases of a classifier towards the majority class, minority samples are over-sampled at a fractional rate. Such implementation is shown to provide more robust classification boundaries. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class tabular datasets. The results as measured by average class specific accuracy (ACSA) clearly indicate that the proposed method provides better classification accuracy (improvement in the range of 0.7% to 49.27%) as compared to the baseline classifier.
Are socially-aware trajectory prediction models really socially-aware?
Authors: Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi, Mohammad Saneian, Seyed-Mohsen Moosavi-Dezfooli, Alexandre Alahi
Date: 2021-08-24 00:00:00
Tasks: Adversarial Robustness, Autonomous Navigation, Collision Avoidance, Motion Forecasting, Prediction, Trajectory Prediction

Our field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustness has not been carefully studied. In this paper, we introduce a socially-attended attack to assess the social understanding of prediction models in terms of collision avoidance. An attack is a small yet carefully-crafted perturbations to fail predictors. Technically, we define collision as a failure mode of the output, and propose hard- and soft-attention mechanisms to guide our attack. Thanks to our attack, we shed light on the limitations of the current models in terms of their social understanding. We demonstrate the strengths of our method on the recent trajectory prediction models. Finally, we show that our attack can be employed to increase the social understanding of state-of-the-art models. The code is available online: https://s-attack.github.io/
A QuadTree Image Representation for Computational Pathology
Authors: Rob Jewsbury, Abhir Bhalerao, Nasir Rajpoot
Date: 2021-08-24 00:00:00

The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to be split up into image tiles or patches so modern convolutional neural networks (CNNs) can process them. In this work, we present a method to generate an interpretable image representation of computational pathology images using quadtrees and a pipeline to use these representations for highly accurate downstream classification. To the best of our knowledge, this is the first attempt to use quadtrees for pathology image data. We show it is highly accurate, able to achieve as good results as the currently widely adopted tissue mask patch extraction methods all while using over 38% less data.
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Authors: Zachary Teed, Jia Deng
Date: 2021-08-24 00:00:00
Proceeding: NeurIPS 2021 12

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. DROID-SLAM is accurate, achieving large improvements over prior work, and robust, suffering from substantially fewer catastrophic failures. Despite training on monocular video, it can leverage stereo or RGB-D video to achieve improved performance at test time. The URL to our open source code is https://github.com/princeton-vl/DROID-SLAM.
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Authors: Kuniaki Saito, Donghyun Kim, Piotr Teterwak, Stan Sclaroff, Trevor Darrell, Kate Saenko
Date: 2021-08-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, image-classification, Image Classification, Semantic Segmentation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achieving high accuracy and avoiding negative transfer. Supervised hyper-parameter validation is not possible without labeled target data, which raises the question: How can we validate unsupervised adaptation techniques in a realistic way? We first empirically analyze existing criteria and demonstrate that they are not very effective for tuning hyper-parameters. Intuitively, a well-trained source classifier should embed target samples of the same class nearby, forming dense neighborhoods in feature space. Based on this assumption, we propose a novel unsupervised validation criterion that measures the density of soft neighborhoods by computing the entropy of the similarity distribution between points. Our criterion is simpler than competing validation methods, yet more effective; it can tune hyper-parameters and the number of training iterations in both image classification and semantic segmentation models. The code used for the paper will be available at \url{https://github.com/VisionLearningGroup/SND}.
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision
Authors: Ning-Hsu Wang, Ren Wang, Yu-Lun Liu, Yu-Hao Huang, Yu-Lin Chang, Chia-Ping Chen, Kevin Jou
Date: 2021-08-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: All, Depth Estimation

Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another cue for depth estimation. In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack). We design a shared architecture to exploit the relationship between depth and AiF estimation. As a result, the proposed method can be trained either supervisedly with ground truth depth, or \emph{unsupervisedly} with AiF images as supervisory signals. We show in various experiments that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and also has higher efficiency in inference time.
imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose
Authors: Thiemo Alldieck, Hongyi Xu, Cristian Sminchisescu
Date: 2021-08-24 00:00:00
Proceeding: ICCV 2021 10

We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function. In contrast to prior work, we model the full human body implicitly as a function zero-level-set and without the use of an explicit template mesh. We propose a novel network architecture and a learning paradigm, which make it possible to learn a detailed implicit generative model of human pose, shape, and semantics, on par with state-of-the-art mesh-based models. Our model features desired detail for human models, such as articulated pose including hand motion and facial expressions, a broad spectrum of shape variations, and can be queried at arbitrary resolutions and spatial locations. Additionally, our model has attached spatial semantics making it straightforward to establish correspondences between different shape instances, thus enabling applications that are difficult to tackle using classical implicit representations. In extensive experiments, we demonstrate the model accuracy and its applicability to current research problems.
Next-generation perception system for automated defects detection in composite laminates via polarized computational imaging
Authors: Yuqi Ding, Jinwei Ye, Corina Barbalata, James Oubre, Chandler Lemoine, Jacob Agostinho, Genevieve Palardy
Date: 2021-08-24 00:00:00

Finishing operations on large-scale composite components like wind turbine blades, including trimming and sanding, often require multiple workers and part repositioning. In the composites manufacturing industry, automation of such processes is challenging, as manufactured part geometry may be inconsistent and task completion is based on human judgment and experience. Implementing a mobile, collaborative robotic system capable of performing finishing tasks in dynamic and uncertain environments would improve quality and lower manufacturing costs. To complete the given tasks, the collaborative robotic team must properly understand the environment and detect irregularities in the manufactured parts. In this paper, we describe the initial implementation and demonstration of a polarized computational imaging system to identify defects in composite laminates. As the polarimetric images are highly relevant to the surface micro-geometry, they can be used to detect surface defects that are not visible in conventional color images. The proposed vision system successfully identifies defect types and surface characteristics (e.g., pinholes, voids, scratches, resin flash) for different glass fiber and carbon fiber laminates.
Reconcile Prediction Consistency for Balanced Object Detection
Authors: Keyang Wang, Lei Zhang
Date: 2021-08-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Object, object-detection, Object Detection, Prediction, regression

Classification and regression are two pillars of object detectors. In most CNN-based detectors, these two pillars are optimized independently. Without direct interactions between them, the classification loss and the regression loss can not be optimized synchronously toward the optimal direction in the training phase. This clearly leads to lots of inconsistent predictions with high classification score but low localization accuracy or low classification score but high localization accuracy in the inference phase, especially for the objects of irregular shape and occlusion, which severely hurts the detection performance of existing detectors after NMS. To reconcile prediction consistency for balanced object detection, we propose a Harmonic loss to harmonize the optimization of classification branch and localization branch. The Harmonic loss enables these two branches to supervise and promote each other during training, thereby producing consistent predictions with high co-occurrence of top classification and localization in the inference phase. Furthermore, in order to prevent the localization loss from being dominated by outliers during training phase, a Harmonic IoU loss is proposed to harmonize the weight of the localization loss of different IoU-level samples. Comprehensive experiments on benchmarks PASCAL VOC and MS COCO demonstrate the generality and effectiveness of our model for facilitating existing object detectors to state-of-the-art accuracy.
A Benchmark for Spray from Nearby Cutting Vehicles
Authors: Stefanie Walz, Mario Bijelic, Florian Kraus, Werner Ritter, Martin Simon, Igor Doric
Date: 2021-08-24 00:00:00
Tasks: Autonomous Driving, Benchmarking

Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to assess the disturbances caused by spray. The analysis covers an automotive RGB camera and two different LiDAR systems, as well as downstream detection algorithms based on YOLOv3 and PV-RCNN. In a common scenario of a closely cutting vehicle, it is visible that the distortions are severely affecting the perception stack up to four seconds showing the necessity of benchmarking the influences of spray.
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
Authors: Zhizhong Huang, Junping Zhang, Yi Zhang, Hongming Shan
Date: 2021-08-24 00:00:00
Tasks: Denoising, Diagnostic

LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Various deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization
Authors: Cheng Zhang, Zhaopeng Cui, Cai Chen, Shuaicheng Liu, Bing Zeng, Hujun Bao, yinda zhang
Date: 2021-08-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Diversity, Graph Neural Network, Object, Relation, Scene Understanding

Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods. In this paper, we propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. In order to fully utilize the rich context information, we design a novel graph neural network based context model to predict the relationship among objects and room layout, and a differentiable relationship-based optimization module to optimize object arrangement with well-designed objective functions on-the-fly. Realizing the existing data are either with incomplete ground truth or overly-simplified scene, we present a new synthetic dataset with good diversity in room layout and furniture placement, and realistic image quality for total panoramic 3D scene understanding. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement. Code is available at https://chengzhag.github.io/publication/dpc.
Pen Spinning Hand Movement Analysis Using MediaPipe Hands
Authors: Tung-Lin Wu, Taishi Senda
Date: 2021-08-24 00:00:00

We challenged to get data about hand movement in pen spinning using MediaPipe Hands and OpenCV. The purpose is to create a system that can be used to objectively evaluate the performance of pen spinning competitions. Evaluation of execution, smoothness, and control in competitions are quite difficult and often with subjectivity. Therefore, we aimed to fully automate the process by using objective numerical values for evaluation. Uncertainty still exists in MediaPipe's skeletal recognition, and it tends to be more difficult to recognize in brightly colored backgrounds. However, we could improve the recognition accuracy by changing the saturation and brightness in the program. Furthermore, automatic detection and adjustment of brightness is now possible. As the next step to systematize the evaluation of pen spinning using objective numerical values, we adopted "hand movements". We were able to visualize the ups and downs of the hand movements by calculating the standard deviation and L2 norm of the hand's coordinates in each frame. The results of hand movements are quite accurate, and we feel that it is a big step toward our goal. In the future, we would like to make great efforts to fully automate the grading of pen spinning.
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
Authors: Fadi Boutros, Patrick Siebke, Marcel Klemt, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Date: 2021-08-24 00:00:00
Tasks: Face Recognition, Knowledge Distillation, Lightweight Face Recognition, Neural Architecture Search

Deep neural networks have rapidly become the mainstream method for face recognition (FR). However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices. In this work, we present an extremely lightweight and accurate FR solution, namely PocketNet. We utilize neural architecture search to develop a new family of lightweight face-specific architectures. We additionally propose a novel training paradigm based on knowledge distillation (KD), the multi-step KD, where the knowledge is distilled from the teacher model to the student model at different stages of the training maturity. We conduct a detailed ablation study proving both, the sanity of using NAS for the specific task of FR rather than general object classification, and the benefits of our proposed multi-step KD. We present an extensive experimental evaluation and comparisons with the state-of-the-art (SOTA) compact FR models on nine different benchmarks including large-scale evaluation benchmarks such as IJB-B, IJB-C, and MegaFace. PocketNets have consistently advanced the SOTA FR performance on nine mainstream benchmarks when considering the same level of model compactness. With 0.92M parameters, our smallest network PocketNetS-128 achieved very competitive results to recent SOTA compacted models that contain up to 4M parameters.
Spatio-Temporal Self-Attention Network for Video Saliency Prediction
Authors: Ziqiang Wang, Zhi Liu, Gongyang Li, Yang Wang, Tianhong Zhang, Lihua Xu, Jijun Wang
Date: 2021-08-24 00:00:00
Tasks: Prediction, Saliency Prediction, Video Saliency Prediction

3D convolutional neural networks have achieved promising results for video tasks in computer vision, including video saliency prediction that is explored in this paper. However, 3D convolution encodes visual representation merely on fixed local spacetime according to its kernel size, while human attention is always attracted by relational visual features at different time. To overcome this limitation, we propose a novel Spatio-Temporal Self-Attention 3D Network (STSANet) for video saliency prediction, in which multiple Spatio-Temporal Self-Attention (STSA) modules are employed at different levels of 3D convolutional backbone to directly capture long-range relations between spatio-temporal features of different time steps. Besides, we propose an Attentional Multi-Scale Fusion (AMSF) module to integrate multi-level features with the perception of context in semantic and spatio-temporal subspaces. Extensive experiments demonstrate the contributions of key components of our method, and the results on DHF1K, Hollywood-2, UCF, and DIEM benchmark datasets clearly prove the superiority of the proposed model compared with all state-of-the-art models.