pwc-archive/papers-with-abstracts huggingface dataset:
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
Authors: Martin Sundermeyer, Tomas Hodan, Yann Labbe, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas
Date: 2023-02-25 00:00:00
Tasks: 2D Object Detection, 6D Pose Estimation using RGB, object-detection, Object Detection, Pose Estimation

We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image. In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56.9 AR$_C$ in 2019 (Vidal et al.) and 69.8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83.7 AR$_C$ (GDRNPP). Out of 49 pose estimation methods evaluated since 2019, the top 18 are from 2022. Methods based on point pair features, which were introduced in 2010 and achieved competitive results even in 2020, are now clearly outperformed by deep learning methods. The synthetic-to-real domain gap was again significantly reduced, with 82.7 AR$_C$ achieved by GDRNPP trained only on synthetic images from BlenderProc. The fastest variant of GDRNPP reached 80.5 AR$_C$ with an average time per image of 0.23s. Since most of the recent methods for 6D object pose estimation begin by detecting/segmenting objects, we also started evaluating 2D object detection and segmentation performance based on the COCO metrics. Compared to the Mask R-CNN results from CosyPose in 2020, detection improved from 60.3 to 77.3 AP$_C$ and segmentation from 40.5 to 58.7 AP$_C$. The online evaluation system stays open and is available at: \href{http://bop.felk.cvut.cz/}{bop.felk.cvut.cz}.
Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster
Authors: Cagri Toraman, Izzet Emre Kucukkaya, Oguzhan Ozcelik, Umitcan Sahin
Date: 2023-02-26 00:00:00

The importance of social media is again exposed in the recent tragedy of the 2023 Turkey and Syria earthquake. Many victims who were trapped under the rubble called for help by posting messages in Twitter. We present an interactive tool to provide situational awareness for missing and trapped people, and disaster relief for rescue and donation efforts. The system (i) collects tweets, (ii) classifies the ones calling for help, (iii) extracts important entity tags, and (iv) visualizes them in an interactive map screen. Our initial experiments show that the performance in terms of the F1 score is up to 98.30 for tweet classification, and 84.32 for entity extraction. The demonstration, dataset, and other related files can be accessed at https://github.com/avaapm/deprem
Temporal Segment Transformer for Action Segmentation
Authors: Zhichao Liu, Leshan Wang, Desen Zhou, Jian Wang, Songyang Zhang, Yang Bai, Errui Ding, Rui Fan
Date: 2023-02-25 00:00:00
Tasks: Action Segmentation, Denoising, Segmentation

Recognizing human actions from untrimmed videos is an important task in activity understanding, and poses unique challenges in modeling long-range temporal relations. Recent works adopt a predict-and-refine strategy which converts an initial prediction to action segments for global context modeling. However, the generated segment representations are often noisy and exhibit inaccurate segment boundaries, over-segmentation and other problems. To deal with these issues, we propose an attention based approach which we call \textit{temporal segment transformer}, for joint segment relation modeling and denoising. The main idea is to denoise segment representations using attention between segment and frame representations, and also use inter-segment attention to capture temporal correlations between segments. The refined segment representations are used to predict action labels and adjust segment boundaries, and a final action segmentation is produced based on voting from segment masks. We show that this novel architecture achieves state-of-the-art accuracy on the popular 50Salads, GTEA and Breakfast benchmarks. We also conduct extensive ablations to demonstrate the effectiveness of different components of our design.
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Authors: Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi, Daniele Ravì
Date: 2023-02-25 00:00:00
Tasks: Image Retrieval, Retrieval

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.
CASIA-Iris-Africa: A Large-scale African Iris Image Database
Authors: Jawad Muhammad, Yunlong Wang, Junxing Hu, Kunbo Zhang, Zhenan Sun
Date: 2023-02-25 00:00:00
Tasks: Iris Recognition

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes. Research on iris biometrics has progressed tremendously, partly due to publicly available iris databases. Various databases have been available to researchers that address pressing iris biometric challenges such as constraint, mobile, multispectral, synthetics, long-distance, contact lenses, liveness detection, etc. However, these databases mostly contain subjects of Caucasian and Asian docents with very few Africans. Despite many investigative studies on racial bias in face biometrics, very few studies on iris biometrics have been published, mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain. Furthermore, most of these databases contain a relatively small number of subjects and labelled images. This paper proposes a large-scale African database named CASIA-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans. The database contains 28,717 images of 1023 African subjects (2046 iris classes) with age, gender, and ethnicity attributes that can be useful in demographically sensitive studies of Africans. Sets of specific application protocols are incorporated with the database to ensure the database's variability and scalability. Performance results of some open-source SOTA algorithms on the database are presented, which will serve as baseline performances. The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms. The database will be made available on our website: http://www.idealtest.org.
TBFormer: Two-Branch Transformer for Image Forgery Localization
Authors: Yaqi Liu, Binbin Lv, Xin Jin, Xiaoyu Chen, Xiaokun Zhang
Date: 2023-02-25 00:00:00
Tasks: Decoder, Vocal Bursts Valence Prediction

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.
Introducing Depth into Transformer-based 3D Object Detection
Authors: Hao Zhang, Hongyang Li, Ailing Zeng, Feng Li, Shilong Liu, Xingyu Liao, Lei Zhang
Date: 2023-02-25 00:00:00
Tasks: 3D Object Detection, Auxiliary Learning, Contrastive Learning, Object, object-detection, Object Detection

In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: large depth translation errors and duplicate predictions along depth axes. To mitigate these issues, we propose two key solutions within DAT. To address the first issue, we introduce a Depth-Aware Spatial Cross-Attention (DA-SCA) module that incorporates depth information into spatial cross-attention when lifting image features to 3D space. To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss. First, based on their reference points, we organize features as a Bird's-Eye-View (BEV) feature map. Then, we sample positive and negative features along each object ray that connects an object and a camera and train the model to distinguish between them. The proposed DA-SCA and DNS methods effectively alleviate these two problems. We show that DAT is a versatile method that enhances the performance of all three popular models, BEVFormer, DETR3D, and PETR. Our evaluation on BEVFormer demonstrates that DAT achieves a significant improvement of +2.8 NDS on nuScenes val under the same settings. Moreover, when using pre-trained VoVNet-99 as the backbone, DAT achieves strong results of 60.0 NDS and 51.5 mAP on nuScenes test. Our code will be soon.
Raw Image Reconstruction with Learned Compact Metadata
Authors: YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex Kot, Bihan Wen
Date: 2023-02-25 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Compression, Image Reconstruction, Quantization

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images.
Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search
Authors: Benzhi Wang, Yang Yang, Jinlin Wu, Guo-Jun Qi, Zhen Lei
Date: 2023-02-25 00:00:00
Proceeding: ICCV 2023 1
Tasks: Person Search

Weakly supervised person search aims to jointly detect and match persons with only bounding box annotations. Existing approaches typically focus on improving the features by exploring relations of persons. However, scale variation problem is a more severe obstacle and under-studied that a person often owns images with different scales (resolutions). On the one hand, small-scale images contain less information of a person, thus affecting the accuracy of the generated pseudo labels. On the other hand, the similarity of cross-scale images is often smaller than that of images with the same scale for a person, which will increase the difficulty of matching. In this paper, we address this problem by proposing a novel one-step framework, named Self-similarity driven Scale-invariant Learning (SSL). Scale invariance can be explored based on the self-similarity prior that it shows the same statistical properties of an image at different scales. To this end, we introduce a Multi-scale Exemplar Branch to guide the network in concentrating on the foreground and learning scale-invariant features by hard exemplars mining. To enhance the discriminative power of the features in an unsupervised manner, we introduce a dynamic multi-label prediction which progressively seeks true labels for training. It is adaptable to different types of unlabeled data and serves as a compensation for clustering based strategy. Experiments on PRW and CUHK-SYSU databases demonstrate the effectiveness of our method.
Electric Vehicle Sales Forecasting Model Considering Green Premium: A Chinese Market-based Perspective
Authors: Zhi Li, Hang Fan, Shuyan Dong
Date: 2023-02-27 00:00:00

"Green Premiums" which means the difference in cost between emissions-emitting technology and zero-emissions or emissions-reducing technology is significant for those renewable energy technology to address the climate change challenge facing the world in this century. China's Electrical Vehicles (EVs) industry is the first to cross the green premium into the commercialization stage, prompting its market size to exceed that of the US and EU combined, making it the most inspiring case in global carbon reduction practices. This study, which is based on first-hand data from industry research and innovatively constructs a multi-factor green premium model for EVs based on Total Cost of Ownership (TCO) analysis, finds that EVs currently have a higher green premium than Internal Combustion Engine Vehicles (ICEVs) and is expected that short-range EVs will be the first to achieve parity in acquisition costs by 2025 and long-range EVs by 2030. Further, this paper constructs a generalized Bass diffusion model considering the green premium, selects the time series data of EVs diffusion in the Chinese market from 2010-2021, and uses a genetic algorithm to fit the parameters to predict the EVs market penetration in the next ten years under different scenarios. The model prediction results show that EVs are successful innovative diffusion products and the market penetration rate depends largely on their green premium. The Chinese EVs market may experience a slowdown or even a decline in growth in the short term, but will maintain high growth in the medium to long term, with annual sales expected to reach 10.77 million units by 2030 and a penetration rate of about 39%.
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data
Authors: Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Lydia Chougar, Didier Dormont, Romain Valabregue, Ninon Burgos, Stéphane Lehéricy, Daniel Racoceanu, Olivier Colliot, the ICEBERG Study Group
Date: 2023-02-25 00:00:00
Tasks: Disentanglement, Segmentation

One often lacks sufficient annotated samples for training deep segmentation models. This is in particular the case for less common imaging modalities such as Quantitative Susceptibility Mapping (QSM). It has been shown that deep models tend to fit the target function from low to high frequencies. One may hypothesize that such property can be leveraged for better training of deep learning models. In this paper, we exploit this property to propose a new training method based on frequency-domain disentanglement. It consists of two main steps: i) disentangling the image into high- and low-frequency parts and feature learning; ii) frequency-domain fusion to complete the task. The approach can be used with any backbone segmentation network. We apply the approach to the segmentation of the red and dentate nuclei from QSM data which is particularly relevant for the study of parkinsonian syndromes. We demonstrate that the proposed method provides considerable performance improvements for these tasks. We further applied it to three public datasets from the Medical Segmentation Decathlon (MSD) challenge. For two MSD tasks, it provided smaller but still substantial improvements (up to 7 points of Dice), especially under small training set situations.
Parameter Estimation-Based Extended Observer for Linear Systems with Polynomial Overparameterization
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2023-02-27 00:00:00
Tasks: parameter estimation

We consider a class of uncertain linear time-invariant overparametrized systems affected by bounded disturbances, which are described by a known exosystem with unknown initial conditions. For such systems an exponentially stable extended adaptive observer is proposed, which, unlike existing solutions, simultaneously: (i) allows one to reconstruct original (physical) states of the system represented in arbitrarily chosen state-space form rather than virtual states of the observer canonical form; (ii) ensures convergence of the state observation error to zero under weak requirement of the regressor finite excitation; (iii) does not include Luenberger correction gain and forms states estimate using algebraic rather than differential equation; (iv) additionally reconstructs the unmeasured external disturbance. The proposed solution is based on the new parametrizations to identify the observer parameters obtained with the help of the heterogeneous mappings and the dynamic regressor extension and mixing procedure. Illustrative simulations support obtained theoretical results.
Software principles and concepts applied in the implementation of cyber-physical systems for real-time advanced process control
Authors: Anders H. D. Andersen, Zhanhao Zhang, Steen Hørsholt, Tobias K. S. Ritschel, John Bagterp Jørgensen
Date: 2023-02-27 00:00:00

Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this paper, we use simple examples to describe the software principles and concepts used in the implementation of such systems. The key software principles are 1) shared data in the form of a database, files, or shared memory, 2) timers and threads for concurrent periodic execution of tasks, and 3) network communication between the control system and the process, and communication between the control system and the internet, e.g., the cloud to enable remote monitoring and commands. We show how to implement such systems for Linux operating systems applying the C programming language and we also comment on the implementation using the Python programming language. Finally, we present a complete simulation experiment using a real-time simulator.
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
Authors: Jinzhao Luo, Lu Zhou, Guibo Zhu, Guojing Ge, Beiying Yang, Jinqiao Wang
Date: 2023-02-25 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition

Skeleton-based action recognition has become popular in recent years due to its efficiency and robustness. Most current methods adopt graph convolutional network (GCN) for topology modeling, but GCN-based methods are limited in long-distance correlation modeling and generalizability. In contrast, the potential of convolutional neural network (CNN) for topology modeling has not been fully explored. In this paper, we propose a novel CNN architecture, Temporal-Channel Topology Enhanced Network (TCTE-Net), to learn spatial and temporal topologies for skeleton-based action recognition. The TCTE-Net consists of two modules: the Temporal-Channel Focus module, which learns a temporal-channel focus matrix to identify the most critical feature representations, and the Dynamic Channel Topology Attention module, which dynamically learns spatial topological features, and fuses them with an attention mechanism to model long-distance channel-wise topology. We conduct experiments on NTU RGB+D, NTU RGB+D 120, and FineGym datasets. TCTE-Net shows state-of-the-art performance compared to CNN-based methods and achieves superior performance compared to GCN-based methods. The code is available at https://github.com/aikuniverse/TCTE-Net.
Suitability of Quantized DEVS LIM Methods for Simulation of Power Systems
Authors: Navid Gholizadeh, Joseph M. Hood, Roger A. Dougal
Date: 2023-02-26 00:00:00
Tasks: Computational Efficiency

The suitability of the QDL method for analyzing the performance of ac power systems has been evaluated by application to a microgrid. The QDL method is based on a combination of Quantized State Systems (QSS) methods and the Latency Insertion Method (LIM). The accuracy and computational intensity of QDL simulations were evaluated relative to an industry-standard reference method. The key advantages expected of the QDL approach -- including high computational efficiency when the system is operating in steady-state and, when not in steady-state, the need to update only those states that have been affected by quantum level changes of connected states. The expected advantages were largely realized, but with some complications that remain unresolved and require further research such as limit cycle oscillations that emerge in some states after disturbances that should have returned to stationarity. Also, the method in its current state may not be feasible for fault analysis because of the low computational efficiency that results when large disturbances (e.g. faults) create large excursions simultaneously in many system states. The relative strengths and weaknesses of the method are discussed, and some improvements to the method are proposed to overcome the weaknesses. The revelation of observed problems is intended to inspire additional research to overcome those problems.