pwc-archive/papers-with-abstracts huggingface dataset:
Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based sky-segmentation in urban canyon
Authors: Jingrong Wang, Bo Xu, Ronghe Jin, Shoujian Zhang, Kefu Gao, Jingnan Liu
Date: 2024-04-17 00:00:00
Tasks: Autonomous Driving

Accurate, continuous, and reliable positioning is a critical component of achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view images segmentation algorithm based on Fully Convolutional Network (FCN) is proposed for GNSS NLOS detection. Building upon this, a novel NLOS detection and mitigation algorithm (named S-NDM) is extended to the tightly coupled Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and visual feature system which is called Sky-GVIO, with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system harmonizes Single Point Positioning (SPP) with Real-Time Kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of S-NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP-related and RTK-related models. The results exhibit that Sky-GVIO system achieves meter-level accuracy under SPP mode and sub-decimeter precision with RTK, surpassing the performance of GNSS/INS/Vision frameworks devoid of S-NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration at https://github.com/whuwangjr/sky-view-images .
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
Authors: Yongdong Luo, Haojia Lin, Xiawu Zheng, Yigeng Jiang, Fei Chao, Jie Hu, Guannan Jiang, Songan Zhang, Rongrong Ji
Date: 2024-04-17 00:00:00
Tasks: 3D dense captioning, 3D visual grounding, Dense Captioning, Visual Grounding

3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships. Therefore, existing approaches adopt the two-stage "detect-then-describe/discriminate" pipeline, which relies heavily on the performance of the detector, resulting in suboptimal performance. Inspired by DETR, we propose a unified framework, 3DGCTR, to jointly solve these two distinct but closely related tasks in an end-to-end fashion. The key idea is to reconsider the prompt-based localization ability of the 3DVG model. In this way, the 3DVG model with a well-designed prompt as input can assist the 3DDC task by extracting localization information from the prompt. In terms of implementation, we integrate a Lightweight Caption Head into the existing 3DVG network with a Caption Text Prompt as a connection, effectively harnessing the existing 3DVG model's inherent localization capacity, thereby boosting 3DDC capability. This integration facilitates simultaneous multi-task training on both tasks, mutually enhancing their performance. Extensive experimental results demonstrate the effectiveness of this approach. Specifically, on the ScanRefer dataset, 3DGCTR surpasses the state-of-the-art 3DDC method by 4.3% in CIDEr@0.5IoU in MLE training and improves upon the SOTA 3DVG method by 3.16% in Acc@0.25IoU. The codes are at https://github.com/Leon1207/3DGCTR.
Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection
Authors: Ying Zhang, Yuezun Li, Bo Peng, Jiaran Zhou, Huiyu Zhou, Junyu Dong
Date: 2024-04-17 00:00:00
Tasks: Decoder, Diversity, Video Inpainting

The task of video inpainting detection is to expose the pixel-level inpainted regions within a video sequence. Existing methods usually focus on leveraging spatial and temporal inconsistencies. However, these methods typically employ fixed operations to combine spatial and temporal clues, limiting their applicability in different scenarios. In this paper, we introduce a novel Multilateral Temporal-view Pyramid Transformer ({\em MumPy}) that collaborates spatial-temporal clues flexibly. Our method utilizes a newly designed multilateral temporal-view encoder to extract various collaborations of spatial-temporal clues and introduces a deformable window-based temporal-view interaction module to enhance the diversity of these collaborations. Subsequently, we develop a multi-pyramid decoder to aggregate the various types of features and generate detection maps. By adjusting the contribution strength of spatial and temporal clues, our method can effectively identify inpainted regions. We validate our method on existing datasets and also introduce a new challenging and large-scale Video Inpainting dataset based on the YouTube-VOS dataset, which employs several more recent inpainting methods. The results demonstrate the superiority of our method in both in-domain and cross-domain evaluation scenarios.
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
Authors: Mohammad Shiri, Monalika Padma Reddy, Jiangwen Sun
Date: 2024-04-17 00:00:00
Tasks: Classification, Contrastive Learning, Histopathological Image Classification, image-classification, Image Classification, Specificity, Transfer Learning

Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer. Breast tissue histopathological examination is critical in diagnosing and classifying breast cancer. Although existing methods have shown promising results, there is still room for improvement in the classification accuracy and generalization of IDC using histopathology images. We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of invasive ductal carcinoma in terms of accuracy and generalization by leveraging the inherent strengths and advantages of both transfer learning, i.e., pre-trained vision transformer, and supervised contrastive learning. Our results on a benchmark breast cancer dataset demonstrate that SupCon-Vit achieves state-of-the-art performance in IDC classification, with an F1-score of 0.8188, precision of 0.7692, and specificity of 0.8971, outperforming existing methods. In addition, the proposed model demonstrates resilience in scenarios with minimal labeled data, making it highly efficient in real-world clinical settings where labelled data is limited. Our findings suggest that supervised contrastive learning in conjunction with pre-trained vision transformers appears to be a viable strategy for an accurate classification of IDC, thus paving the way for a more efficient and reliable diagnosis of breast cancer through histopathological image analysis.
WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing
Authors: Rijun Wang, Guanghao Zhang, Fulong Liang, Bo wang, Xiangwei Mou, Yesheng Chen, Peng Sun, Canjin Wang
Date: 2024-04-17 00:00:00
Tasks: Segmentation

Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark removal processing named WPS-dataset is proposed in this study, which consists of 4863 images. We designed an image acquisition device and assembled it on a bark removal equipment to capture images in real industrial settings. We evaluated the WPS-dataset using six typical segmentation models. The models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. We believe that our dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field.
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model
Authors: Hao Yan, Yuhong Guo
Date: 2024-04-17 00:00:00
Tasks: Federated Learning, Language Modeling, Language Modelling

Federated learning aims to tackle the ``isolated data island" problem, where it trains a collective model from physically isolated clients while safeguarding the privacy of users' data. However, supervised federated learning necessitates that each client labels their data for training, which can be both time-consuming and resource-intensive, and may even be impractical for edge devices. Moreover, the training and transmission of deep models present challenges to the computation and communication capabilities of the clients. To address these two inherent challenges in supervised federated learning, we propose a novel lightweight unsupervised federated learning approach that leverages unlabeled data on each client to perform lightweight model training and communication by harnessing pretrained vision-language models, such as CLIP. By capitalizing on the zero-shot prediction capability and the well-trained image encoder of the pre-trained CLIP model, we have carefully crafted an efficient and resilient self-training approach. This method refines the initial zero-shot predicted pseudo-labels of unlabeled instances through the sole training of a linear classifier on top of the fixed image encoder. Additionally, to address data heterogeneity within each client, we propose a class-balanced text feature sampling strategy for generating synthetic instances in the feature space to support local training. Experiments are conducted on multiple benchmark datasets. The experimental results demonstrate that our proposed method greatly enhances model performance in comparison to CLIP's zero-shot predictions and even outperforms supervised federated learning benchmark methods given limited computational and communication overhead.
TaCOS: Task-Specific Camera Optimization with Simulation
Authors: Chengyang Yan, Donald G. Dansereau
Date: 2024-04-17 00:00:00

The performance of perception tasks is heavily influenced by imaging systems. However, designing cameras with high task performance is costly, requiring extensive camera knowledge and experimentation with physical hardware. Additionally, cameras and perception tasks are mostly designed in isolation, whereas recent methods that jointly design cameras and tasks have shown improved performance. Therefore, we present a novel end-to-end optimization approach that co-designs cameras with specific vision tasks. This method combines derivative-free and gradient-based optimizers to support both continuous and discrete camera parameters within manufacturing constraints. We leverage recent computer graphics techniques and physical camera characteristics to simulate the cameras in virtual environments, making the design process cost-effective. We validate our simulations against physical cameras and provide a procedurally generated virtual environment. Our experiments demonstrate that our method designs cameras that outperform common off-the-shelf options, and more efficiently compared to the state-of-the-art approach, requiring only 2 minutes to design a camera on an example experiment compared with 67 minutes for the competing method. Designed to support the development of cameras under manufacturing constraints, multiple cameras, and unconventional cameras, we believe this approach can advance the fully automated design of cameras.
NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval
Authors: Sanggeon Yun, Ryozo Masukawa, Sungheon Jeong, Mohsen Imani
Date: 2024-04-17 00:00:00
Tasks: Image Retrieval, Retrieval

Customizable image retrieval from large datasets remains a critical challenge, particularly when preserving spatial relationships within images. Traditional hashing methods, primarily based on deep learning, often fail to capture spatial information adequately and lack transparency. In this paper, we introduce NeuroHash, a novel neuro-symbolic framework leveraging Hyperdimensional Computing (HDC) to enable highly customizable, spatially-aware image retrieval. NeuroHash combines pre-trained deep neural network models with HDC-based symbolic models, allowing for flexible manipulation of hash values to support conditional image retrieval. Our method includes a self-supervised context-aware HDC encoder and novel loss terms for optimizing lower-dimensional bipolar hashing using multilinear hyperplanes. We evaluate NeuroHash on two benchmark datasets, demonstrating superior performance compared to state-of-the-art hashing methods, as measured by mAP@5K scores and our newly introduced metric, mAP@5Kr, which assesses spatial alignment. The results highlight NeuroHash's ability to achieve competitive performance while offering significant advantages in flexibility and customization, paving the way for more advanced and versatile image retrieval systems.
MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training
Authors: Jiayang Li, Junjun Jiang, Pengwei Liang, Jiayi Ma
Date: 2024-04-17 00:00:00
Tasks: Infrared And Visible Image Fusion

In this research, we introduce MaeFuse, a novel autoencoder model designed for infrared and visible image fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain high-level visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and task-specific applications. MaeFuse, however, deviates from the norm. Instead of being driven by downstream tasks, our model utilizes a pretrained encoder from Masked Autoencoders (MAE), which facilities the omni features extraction for low-level reconstruction and high-level vision tasks, to obtain perception friendly features with a low cost. In order to eliminate the domain gap of different modal features and the block effect caused by the MAE encoder, we further develop a guided training strategy. This strategy is meticulously crafted to ensure that the fusion layer seamlessly adjusts to the feature space of the encoder, gradually enhancing the fusion effect. It facilitates the comprehensive integration of feature vectors from both infrared and visible modalities, preserving the rich details inherent in each. MaeFuse not only introduces a novel perspective in the realm of fusion techniques but also stands out with impressive performance across various public datasets.
AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation
Authors: Qing En, Yuhong Guo
Date: 2024-04-17 00:00:00
Tasks: Attribute, Pseudo Label, Segmentation

Lung-infected area segmentation is crucial for assessing the severity of lung diseases. However, existing image-text multi-modal methods typically rely on labour-intensive annotations for model training, posing challenges regarding time and expertise. To address this issue, we propose a novel attribute knowledge-guided framework for unsupervised lung-infected area segmentation (AKGNet), which achieves segmentation solely based on image-text data without any mask annotation. AKGNet facilitates text attribute knowledge learning, attribute-image cross-attention fusion, and high-confidence-based pseudo-label exploration simultaneously. It can learn statistical information and capture spatial correlations between image and text attributes in the embedding space, iteratively refining the mask to enhance segmentation. Specifically, we introduce a text attribute knowledge learning module by extracting attribute knowledge and incorporating it into feature representations, enabling the model to learn statistical information and adapt to different attributes. Moreover, we devise an attribute-image cross-attention module by calculating the correlation between attributes and images in the embedding space to capture spatial dependency information, thus selectively focusing on relevant regions while filtering irrelevant areas. Finally, a self-training mask improvement process is employed by generating pseudo-labels using high-confidence predictions to iteratively enhance the mask and segmentation. Experimental results on a benchmark medical image dataset demonstrate the superior performance of our method compared to state-of-the-art segmentation techniques in unsupervised scenarios.
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Authors: Qi Han, Zhibo Tian, Chengwei Xia, Kun Zhan
Date: 2024-04-17 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Semi-Supervised Image Classification

Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in fully exploiting the potential of unlabeled data. To address this, we employ information entropy neural estimation to utilize the potential of unlabeled samples. Inspired by contrastive learning, the entropy is estimated by maximizing a lower bound on mutual information across different augmented views. Moreover, we theoretically analyze that the information entropy of the posterior of an image classifier is approximated by maximizing the likelihood function of the softmax predictions. Guided by these insights, we optimize our model from both perspectives to ensure that the predicted probability distribution closely aligns with the ground-truth distribution. Given the theoretical connection to information entropy, we name our method InfoMatch. Through extensive experiments, we show its superior performance. The source code is available at https://github.com/kunzhan/InfoMatch.
How to deal with glare for improved perception of Autonomous Vehicles
Authors: Muhammad Z. Alam, Zeeshan Kaleem, Sousso Kelouwani
Date: 2024-04-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Depth Estimation, Lane Detection, Object, object-detection, Object Detection, Object Recognition, Object Tracking

Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception systems can be easily affected by glare in the presence of a bright source of light, such as the sun or the headlights of the oncoming vehicle at night or simply by light reflecting off snow or ice-covered surfaces; scenarios encountered frequently during driving. In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs. We evaluate these glare reduction methods based on various performance metrics of the CV algorithms used by the perception layer. Specifically, we considered object detection, object recognition, object tracking, depth estimation, and lane detection which are crucial for autonomous driving. The experimental findings validate the efficacy of the proposed glare reduction approach, showcasing enhanced performance across diverse perception tasks and remarkable resilience against varying levels of glare.
FairSSD: Understanding Bias in Synthetic Speech Detectors
Authors: Amit Kumar Singh Yadav, Kratika Bhagtani, Davide Salvi, Paolo Bestagini, Edward J. Delp
Date: 2024-04-17 00:00:00
Tasks: Fairness

Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more interpretable, can generalize to detect synthetic speech in the wild and are robust to noise. However, limited work has been done on understanding bias in these detectors. In this work, we examine bias in existing synthetic speech detectors to determine if they will unfairly target a particular gender, age and accent group. We also inspect whether these detectors will have a higher misclassification rate for bona fide speech from speech-impaired speakers w.r.t fluent speakers. Extensive experiments on 6 existing synthetic speech detectors using more than 0.9 million speech signals demonstrate that most detectors are gender, age and accent biased, and future work is needed to ensure fairness. To support future research, we release our evaluation dataset, models used in our study and source code at https://gitlab.com/viper-purdue/fairssd.
Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
Authors: Junbiao Pang, Zailin Dong, Jiaxin Deng, Mengyuan Zhu, Yunwei Zhang
Date: 2024-04-17 00:00:00
Tasks: Management

Parsing Computer-Aided Design (CAD) drawings is a fundamental step for CAD revision, semantic-based management, and the generation of 3D prototypes in both the architecture and engineering industries. Labeling symbols from a CAD drawing is a challenging yet notorious task from a practical point of view. In this work, we propose to label and spot symbols from CAD images that are converted from CAD drawings. The advantage of spotting symbols from CAD images lies in the low requirement of labelers and the low-cost annotation. However, pixel-wise spotting symbols from CAD images is challenging work. We propose a pixel-wise point location via Progressive Gaussian Kernels (PGK) to balance between training efficiency and location accuracy. Besides, we introduce a local offset to the heatmap-based point location method. Based on the keypoints detection, we propose a symbol grouping method to redraw the rectangle symbols in CAD images. We have released a dataset containing CAD images of equipment rooms from telecommunication industrial CAD drawings. Extensive experiments on this real-world dataset show that the proposed method has good generalization ability.
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
Authors: Changbin Li, Kangshuo Li, Yuzhe Ou, Lance M. Kaplan, Audun Jøsang, Jin-Hee Cho, Dong Hyun Jeong, Feng Chen
Date: 2024-04-17 00:00:00
Tasks: Deep Learning, Multi-class Classification

Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL). By placing a grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data. We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs. Our results demonstrate that HENN outperforms its state-of-the-art counterparts based on four image datasets. The code and datasets are available at: https://github.com/Hugo101/HyperEvidentialNN.