pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Accident Detection in Traffic Surveillance Using Deep Learning
Authors: Hadi Ghahremannezhad, Hang Shi, Chengjun Liu
Date: 2022-08-12 00:00:00
Tasks: Deep Learning, Management, Object, object-detection, Object Detection, Object Tracking

Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Therefore, computer vision techniques can be viable tools for automatic accident detection. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tracking based on Kalman filter coupled with the Hungarian algorithm for association, and accident detection by trajectory conflict analysis. A new cost function is applied for object association to accommodate for occlusion, overlapping objects, and shape changes in the object tracking step. The object trajectories are analyzed in terms of velocity, angle, and distance in order to detect different types of trajectory conflicts including vehicle-to-vehicle, vehicle-to-pedestrian, and vehicle-to-bicycle. Experimental results using real traffic video data show the feasibility of the proposed method in real-time applications of traffic surveillance. In particular, trajectory conflicts, including near-accidents and accidents occurring at urban intersections are detected with a low false alarm rate and a high detection rate. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions. The dataset is publicly available at: http://github.com/hadi-ghnd/AccidentDetection.
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
Authors: Rafael Rodriguez-Sanchez, Benjamin A. Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris
Date: 2022-08-12 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning (RL), World Knowledge

We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike existing RL DSLs that ground to \textit{single} elements of a decision-making formalism (e.g., the reward function or policy), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic \textit{partial} world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs demonstrating how different RL methods can exploit the resulting knowledge, encompassing model-free and model-based tabular algorithms, policy gradient and value-based methods, hierarchical approaches, and deep methods.
USB: A Unified Semi-supervised Learning Benchmark for Classification
Authors: Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, RenJie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, Yue Zhang
Date: 2022-08-12 00:00:00
Tasks: General Classification, GPU, Semi-Supervised Image Classification

Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with TorchSSL.
RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation
Authors: Yao Zhang, Yun Xiong, Yiheng Sun, Caihua Shan, Tian Lu, Hui Song, Yangyong Zhu
Date: 2022-08-12 00:00:00
Tasks: Fairness

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.
Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home
Authors: Farshad Ahmadi Asl, Mehmet Bodur
Date: 2022-08-11 00:00:00
Tasks: energy management, Management, regression

The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.
Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation
Authors: Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Date: 2022-08-15 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Changes to hyperparameters can have a dramatic effect on model accuracy. Thus, the tuning of hyperparameters plays an important role in optimizing machine-learning models. An integral part of the hyperparameter-tuning process is the evaluation of model checkpoints, which is done through the use of "validators". In a supervised setting, these validators evaluate checkpoints by computing accuracy on a validation set that has labels. In contrast, in an unsupervised setting, the validation set has no such labels. Without any labels, it is impossible to compute accuracy, so validators must estimate accuracy instead. But what is the best approach to estimating accuracy? In this paper, we consider this question in the context of unsupervised domain adaptation (UDA). Specifically, we propose three new validators, and we compare and rank them against five other existing validators, on a large dataset of 1,000,000 checkpoints. Extensive experimental results show that two of our proposed validators achieve state-of-the-art performance in various settings. Finally, we find that in many cases, the state-of-the-art is obtained by a simple baseline method. To the best of our knowledge, this is the largest empirical study of UDA validators to date. Code is available at https://www.github.com/KevinMusgrave/powerful-benchmarker.
Action Recognition based on Cross-Situational Action-object Statistics
Authors: Satoshi Tsutsui, Xizi Wang, Guangyuan Weng, Yayun Zhang, David Crandall, Chen Yu
Date: 2022-08-15 00:00:00
Tasks: Action Recognition, Object, Open-Ended Question Answering

Machine learning models of visual action recognition are typically trained and tested on data from specific situations where actions are associated with certain objects. It is an open question how action-object associations in the training set influence a model's ability to generalize beyond trained situations. We set out to identify properties of training data that lead to action recognition models with greater generalization ability. To do this, we take inspiration from a cognitive mechanism called cross-situational learning, which states that human learners extract the meaning of concepts by observing instances of the same concept across different situations. We perform controlled experiments with various types of action-object associations, and identify key properties of action-object co-occurrence in training data that lead to better classifiers. Given that these properties are missing in the datasets that are typically used to train action classifiers in the computer vision literature, our work provides useful insights on how we should best construct datasets for efficiently training for better generalization.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
Authors: Marco Huber, Fadi Boutros, Anh Thi Luu, Kiran Raja, Raghavendra Ramachandra, Naser Damer, Pedro C. Neto, Tiago Gonçalves, Ana F. Sequeira, Jaime S. Cardoso, João Tremoço, Miguel Lourenço, Sergio Serra, Eduardo Cermeño, Marija Ivanovska, Borut Batagelj, Andrej Kronovšek, Peter Peer, Vitomir Štruc
Date: 2022-08-15 00:00:00
Tasks: Face Morphing Attack Detection, valid

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people's privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.
Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students
Authors: Hanna Siebert, Marian Himstedt, Mattias Heinrich
Date: 2022-08-15 00:00:00
Tasks: Medical Image Analysis

In order to be able to use artificial intelligence (AI) in medicine without scepticism and to recognise and assess its growing potential, a basic understanding of this topic is necessary among current and future medical staff. Under the premise of "trust through understanding", we developed an innovative online course as a learning opportunity within the framework of the German KI Campus (AI campus) project, which is a self-guided course that teaches the basics of AI for the analysis of medical image data. The main goal is to provide a learning environment for a sufficient understanding of AI in medical image analysis so that further interest in this topic is stimulated and inhibitions towards its use can be overcome by means of positive application experience. The focus was on medical applications and the fundamentals of machine learning. The online course was divided into consecutive lessons, which include theory in the form of explanatory videos, practical exercises in the form of Streamlit and practical exercises and/or quizzes to check learning progress. A survey among the participating medical students in the first run of the course was used to analyse our research hypotheses quantitatively.
Task Oriented Video Coding: A Survey
Authors: Daniel Wood
Date: 2022-08-15 00:00:00
Tasks: Survey

Video coding technology has been continuously improved for higher compression ratio with higher resolution. However, the state-of-the-art video coding standards, such as H.265/HEVC and Versatile Video Coding, are still designed with the assumption the compressed video will be watched by humans. With the tremendous advance and maturation of deep neural networks in solving computer vision tasks, more and more videos are directly analyzed by deep neural networks without humans' involvement. Such a conventional design for video coding standard is not optimal when the compressed video is used by computer vision applications. While the human visual system is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is driven by specific computer vision tasks. In this paper, we explore and summarize recent progress on computer vision task oriented video coding and emerging video coding standard, Video Coding for Machines.
Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body
Authors: Sara Khalili, Hoda Mohammadzade, Mohammad Mahdi Ahmadi
Date: 2022-08-15 00:00:00

One of the possible dangers that older people face in their daily lives is falling. Occlusion is one of the biggest challenges of vision-based fall detection systems and degrades their detection performance considerably. To tackle this problem, we synthesize specifically-designed occluded videos for training fall detection systems using existing datasets. Then, by defining a new cost function, we introduce a framework for weighted training of fall detection models using occluded and un-occluded videos, which can be applied to any learnable fall detection system. Finally, we use both a non-deep and deep model to evaluate the effect of the proposed weighted training method. Experiments show that the proposed method can improve the classification accuracy by 36% for a non-deep model and 55% for a deep model in occlusion conditions. Moreover, it is shown that the proposed training framework can also significantly improve the detection performance of a deep network on normal un-occluded samples.
Multi-modal Transformer Path Prediction for Autonomous Vehicle
Authors: Chia Hong Tseng, Jie Zhang, Min-Te Sun, Kazuya Sakai, Wei-Shinn Ku
Date: 2022-08-15 00:00:00
Tasks: Autonomous Driving, Prediction, Trajectory Forecasting

Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out. In addition, consecutive lane chunks are combined to ensure the lane input to be long enough for path prediction. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
HEFT: Homomorphically Encrypted Fusion of Biometric Templates
Authors: Luke Sperling, Nalini Ratha, Arun Ross, Vishnu Naresh Boddeti
Date: 2022-08-15 00:00:00
Tasks: Dimensionality Reduction

This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit $\ell_2$-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphically Encrypted Fusion of biometric Templates), is custom-designed to overcome the unique constraint imposed by FHE, namely the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce an FHE-aware algorithm for learning the linear projection matrix to mitigate errors induced by approximate normalization. Experimental evaluation for template fusion and matching of face and voice biometrics shows that HEFT (i) improves biometric verification performance by 11.07% and 9.58% AUROC compared to the respective unibiometric representations while compressing the feature vectors by a factor of 16 (512D to 32D), and (ii) fuses a pair of encrypted feature vectors and computes its match score against a gallery of size 1024 in 884 ms. Code and data are available at https://github.com/human-analysis/encrypted-biometric-fusion
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
Authors: Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
Date: 2022-08-15 00:00:00

Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the deep generative models, offers aggressive acceleration and better reconstruction in magnetic resonance imaging. Nevertheless, learning the data distribution as prior knowledge and reconstructing the image from limited data remains challenging. In this work, we propose a novel Hankel-k-space generative model (HKGM), which can generate samples from a training set of as little as one k-space data. At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches. Extracting patches from a Hankel matrix enables the generative model to be learned from redundant and low-rank data space. At the iterative reconstruction stage, it is observed that the desired solution obeys the learned prior knowledge. The intermediate reconstruction solution is updated by taking it as the input of the generative model. The updated result is then alternatively operated by imposing low-rank penalty on its Hankel matrix and data consistency con-strain on the measurement data. Experimental results confirmed that the internal statistics of patches within a single k-space data carry enough information for learning a powerful generative model and provide state-of-the-art reconstruction.
A Human-in-the-Middle Attack against Object Detection Systems
Authors: Han Wu, Sareh Rowlands, Johan Wahlstrom
Date: 2022-08-15 00:00:00
Tasks: Autonomous Driving, Deep Learning, object-detection, Object Detection

Object detection systems using deep learning models have become increasingly popular in robotics thanks to the rising power of CPUs and GPUs in embedded systems. However, these models are susceptible to adversarial attacks. While some attacks are limited by strict assumptions on access to the detection system, we propose a novel hardware attack inspired by Man-in-the-Middle attacks in cryptography. This attack generates a Universal Adversarial Perturbations (UAP) and injects the perturbation between the USB camera and the detection system via a hardware attack. Besides, prior research is misled by an evaluation metric that measures the model accuracy rather than the attack performance. In combination with our proposed evaluation metrics, we significantly increased the strength of adversarial perturbations. These findings raise serious concerns for applications of deep learning models in safety-critical systems, such as autonomous driving.
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
Authors: Jia Guo, Jinke Yu, Alexandros Lattas, Jiankang Deng
Date: 2022-08-15 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, regression

Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection. In this paper, we propose to simultaneously reconstruct 3D face mesh in the world space and predict 2D face landmarks on the image plane to address the problem of perspective 3D face reconstruction. Based on the predicted 3D vertices and 2D landmarks, the 6DoF (6 Degrees of Freedom) face pose can be easily estimated by the PnP solver to represent perspective projection. Our approach achieves 1st place on the leader-board of the ECCV 2022 WCPA challenge and our model is visually robust under different identities, expressions and poses. The training code and models are released to facilitate future research.
An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection
Authors: Xinzhu Ma, Yuan Meng, Yinmin Zhang, Lei Bai, Jun Hou, Shuai Yi, Wanli Ouyang
Date: 2022-08-15 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection

Image-based 3D detection is an indispensable component of the perception system for autonomous driving. However, it still suffers from the unsatisfying performance, one of the main reasons for which is the limited training data. Unfortunately, annotating the objects in the 3D space is extremely time/resource-consuming, which makes it hard to extend the training set arbitrarily. In this work, we focus on the semi-supervised manner and explore the feasibility of a cheaper alternative, i.e. pseudo-labeling, to leverage the unlabeled data. For this purpose, we conduct extensive experiments to investigate whether the pseudo-labels can provide effective supervision for the baseline models under varying settings. The experimental results not only demonstrate the effectiveness of the pseudo-labeling mechanism for image-based 3D detection (e.g. under monocular setting, we achieve 20.23 AP for moderate level on the KITTI-3D testing set without bells and whistles, improving the baseline model by 6.03 AP), but also show several interesting and noteworthy findings (e.g. the models trained with pseudo-labels perform better than that trained with ground-truth annotations based on the same training data). We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting. The codes, pseudo-labels, and pre-trained models will be publicly available.