pwc-archive/papers-with-abstracts huggingface dataset:
Sliced Wasserstein Estimation with Control Variates
Authors: Khai Nguyen, Nhat Ho
Date: 2023-04-30 00:00:00

The sliced Wasserstein (SW) distances between two probability measures are defined as the expectation of the Wasserstein distance between two one-dimensional projections of the two measures. The randomness comes from a projecting direction that is used to project the two input measures to one dimension. Due to the intractability of the expectation, Monte Carlo integration is performed to estimate the value of the SW distance. Despite having various variants, there has been no prior work that improves the Monte Carlo estimation scheme for the SW distance in terms of controlling its variance. To bridge the literature on variance reduction and the literature on the SW distance, we propose computationally efficient control variates to reduce the variance of the empirical estimation of the SW distance. The key idea is to first find Gaussian approximations of projected one-dimensional measures, then we utilize the closed-form of the Wasserstein-2 distance between two Gaussian distributions to design the control variates. In particular, we propose using a lower bound and an upper bound of the Wasserstein-2 distance between two fitted Gaussians as two computationally efficient control variates. We empirically show that the proposed control variate estimators can help to reduce the variance considerably when comparing measures over images and point-clouds. Finally, we demonstrate the favorable performance of the proposed control variate estimators in gradient flows to interpolate between two point-clouds and in deep generative modeling on standard image datasets, such as CIFAR10 and CelebA.
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Authors: Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu
Date: 2023-04-30 00:00:00
Tasks: Anomaly Detection, Classification, One-Class Classification, Self-Supervised Learning

Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing feature embedding and struggle to build compact descriptions of normal features when performing one-class classification. One direct consequence of this is that most models perform poorly in detecting logical anomalies which violate contextual relationships. Focusing on more effective and comprehensive anomaly detection, we propose a network based on self-supervised learning and self-attentive graph convolution (SLSG) for anomaly detection. SLSG uses a generative pre-training network to assist the encoder in learning the embedding of normal patterns and the reasoning of position relationships. Subsequently, SLSG introduces the pseudo-prior knowledge of anomaly through simulated abnormal samples. By comparing the simulated anomalies, SLSG can better summarize the normal features and narrow down the hypersphere used for one-class classification. In addition, with the construction of a more general graph structure, SLSG comprehensively models the dense and sparse relationships among elements in the image, which further strengthens the detection of logical anomalies. Extensive experiments on benchmark datasets show that SLSG achieves superior anomaly detection performance, demonstrating the effectiveness of our method.
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection
Authors: Su Pang, Daniel Morris, Hayder Radha
Date: 2023-04-30 00:00:00
Tasks: 3D Object Detection, Decoder, Object, object-detection, Object Detection

Despite radar's popularity in the automotive industry, for fusion-based 3D object detection, most existing works focus on LiDAR and camera fusion. In this paper, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. Our TransCAR consists of two modules. The first module learns 2D features from surround-view camera images and then uses a sparse set of 3D object queries to index into these 2D features. The vision-updated queries then interact with each other via transformer self-attention layer. The second module learns radar features from multiple radar scans and then applies transformer decoder to learn the interactions between radar features and vision-updated queries. The cross-attention layer within the transformer decoder can adaptively learn the soft-association between the radar features and vision-updated queries instead of hard-association based on sensor calibration only. Finally, our model estimates a bounding box per query using set-to-set Hungarian loss, which enables the method to avoid non-maximum suppression. TransCAR improves the velocity estimation using the radar scans without temporal information. The superior experimental results of our TransCAR on the challenging nuScenes datasets illustrate that our TransCAR outperforms state-of-the-art Camera-Radar fusion-based 3D object detection approaches.
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization
Authors: Yanpeng Zhao, Siyu Gao, Yunbo Wang, Xiaokang Yang
Date: 2023-04-30 00:00:00
Tasks: Decoder, NeRF, Neural Rendering, Novel View Synthesis, Object, Representation Learning, Scene Understanding

Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaVol, a 3D scene generative model that unifies geometric structures and object-centric learning in a differentiable volume rendering framework. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene, which infers the probability distribution over objects at individual spatial locations. These voxel features evolve over time through a canonical-space deformation function, forming the basis for global representation learning via slot attention. The voxel features and global features are complementary and are both leveraged by a compositional NeRF decoder for volume rendering. DynaVol remarkably outperforms existing approaches for unsupervised dynamic scene decomposition. Once trained, the explicitly meaningful voxel features enable additional capabilities that 2D scene decomposition methods cannot achieve: it is possible to freely edit the geometric shapes or manipulate the motion trajectories of the objects.
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
Authors: Yuheng Li, Jacob Wynne, Jing Wang, Richard L. J. Qiu, Justin Roper, Shaoyan Pan, Ashesh B. Jani, Tian Liu, Pretesh R. Patel, Hui Mao, Xiaofeng Yang
Date: 2023-04-30 00:00:00
Tasks: Self-Supervised Learning

Biparametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection using convolutional neural networks (CNNs). Recently, transformers have achieved competitive performance compared to CNNs in computer vision. Large scale transformers need abundant annotated data for training, which are difficult to obtain in medical imaging. Self-supervised learning (SSL) utilizes unlabeled data to generate meaningful semantic representations without the need for costly annotations, enhancing model performance on tasks with limited labeled data. We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework. Using a large prostate bpMRI dataset with 1500 patients, we first pretrain CSwin transformer using multi-task self-supervised learning to improve data-efficiency and network generalizability. We then finetune using lesion annotations to perform csPCa detection. Five-fold cross validation shows that self-supervised CSwin UNet achieves 0.888 AUC and 0.545 Average Precision (AP), significantly outperforming four comparable models (Swin UNETR, DynUNet, Attention UNet, UNet). Using a separate bpMRI dataset with 158 patients, we evaluate our method robustness to external hold-out data. Self-supervised CSwin UNet achieves 0.79 AUC and 0.45 AP, still outperforming all other comparable methods and demonstrating good generalization to external data.
Image Completion via Dual-path Cooperative Filtering
Authors: Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger
Date: 2023-04-30 00:00:00

Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for restoring images, which predicts the most effective kernels based on the input scene. Motivated by this approach, we address image completion as a filtering problem. Deep feature-level semantic filtering is introduced to fill in missing information, while preserving local structure and generating visually realistic content. In particular, a Dual-path Cooperative Filtering (DCF) model is proposed, where one path predicts dynamic kernels, and the other path extracts multi-level features by using Fast Fourier Convolution to yield semantically coherent reconstructions. Experiments on three challenging image completion datasets show that our proposed DCF outperforms state-of-art methods.
Neural Radiance Fields (NeRFs): A Review and Some Recent Developments
Authors: Mohamed Debbagh
Date: 2023-04-30 00:00:00
Tasks: NeRF, Novel View Synthesis

Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the weights of a fully connected neural network, known as the Multi-Layer Perception(MLP). The method was introduced for the task of novel view synthesis and is able to achieve state-of-the-art photorealistic image renderings from a given continuous viewpoint. NeRFs have become a popular field of research as recent developments have been made that expand the performance and capabilities of the base framework. Recent developments include methods that require less images to train the model for view synthesis as well as methods that are able to generate views from unconstrained and dynamic scene representations.
Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
Authors: Jan Niklas Kolf, Tim Rieber, Jurek Elliesen, Fadi Boutros, Arjan Kuijper, Naser Damer
Date: 2023-04-30 00:00:00
Tasks: Face Recognition, Generative Adversarial Network, Synthetic Face Recognition

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available ( https://github.com/fdbtrs/Synthetic-Face-Recognition ).
Modality-invariant Visual Odometry for Embodied Vision
Authors: Marius Memmel, Roman Bachmann, Amir Zamir
Date: 2023-04-29 00:00:00
Proceeding: CVPR 2023 1
Tasks: Visual Odometry

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Authors: Thuy Dung Nguyen, Anh Duy Nguyen, Kok-Seng Wong, Huy Hieu Pham, Thanh Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen
Date: 2023-04-29 00:00:00
Tasks: Backdoor Attack, Federated Learning

Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, especially backdoor insertion during training. Recently, the edge-case backdoor attack employing the tail of the data distribution has been proposed as a powerful one, raising questions about the shortfall in current defenses' robustness guarantees. Specifically, most existing defenses cannot eliminate edge-case backdoor attacks or suffer from a trade-off between backdoor-defending effectiveness and overall performance on the primary task. To tackle this challenge, we propose FedGrad, a novel backdoor-resistant defense for FL that is resistant to cutting-edge backdoor attacks, including the edge-case attack, and performs effectively under heterogeneous client data and a large number of compromised clients. FedGrad is designed as a two-layer filtering mechanism that thoroughly analyzes the ultimate layer's gradient to identify suspicious local updates and remove them from the aggregation process. We evaluate FedGrad under different attack scenarios and show that it significantly outperforms state-of-the-art defense mechanisms. Notably, FedGrad can almost 100% correctly detect the malicious participants, thus providing a significant reduction in the backdoor effect (e.g., backdoor accuracy is less than 8%) while not reducing the main accuracy on the primary task.
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors
Authors: Walter Zimmer, Joseph Birkner, Marcel Brucker, Huu Tung Nguyen, Stefan Petrovski, Bohan Wang, Alois C. Knoll
Date: 2023-04-29 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection

Current multi-modal object detection approaches focus on the vehicle domain and are limited in the perception range and the processing capabilities. Roadside sensor units (RSUs) introduce a new domain for perception systems and leverage altitude to observe traffic. Cameras and LiDARs mounted on gantry bridges increase the perception range and produce a full digital twin of the traffic. In this work, we introduce InfraDet3D, a multi-modal 3D object detector for roadside infrastructure sensors. We fuse two LiDARs using early fusion and further incorporate detections from monocular cameras to increase the robustness and to detect small objects. Our monocular 3D detection module uses HD maps to ground object yaw hypotheses, improving the final perception results. The perception framework is deployed on a real-world intersection that is part of the A9 Test Stretch in Munich, Germany. We perform several ablation studies and experiments and show that fusing two LiDARs with two cameras leads to an improvement of +1.90 mAP compared to a camera-only solution. We evaluate our results on the A9 infrastructure dataset and achieve 68.48 mAP on the test set. The dataset and code will be available at https://a9-dataset.com to allow the research community to further improve the perception results and make autonomous driving safer.
Polyp-SAM: Transfer SAM for Polyp Segmentation
Authors: Yuheng Li, Mingzhe Hu, Xiaofeng Yang
Date: 2023-04-29 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Transfer Learning

Colon polyps are considered important precursors for colorectal cancer. Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon cancer and improve physician annotation efficiency. While many methods have been proposed for polyp segmentation, training large-scale segmentation networks with limited colonoscopy data remains a challenge. Recently, the Segment Anything Model (SAM) has recently gained much attention in both natural and medical image segmentation. SAM demonstrates superior performance in several image benchmarks and therefore shows great potential for medical image segmentation. In this study, we propose Poly-SAM, a finetuned SAM model for polyp segmentation, and compare its performance to several state-of-the-art polyp segmentation models. We also compare two transfer learning strategies of SAM with and without finetuning its encoders. Evaluated on five public datasets, our Polyp-SAM achieves state-of-the-art performance on two datasets and impressive performance on three datasets, with dice scores all above 88%. This study demonstrates the great potential of adapting SAM to medical image segmentation tasks. We plan to release the code and model weights for this paper at: https://github.com/ricklisz/Polyp-SAM.
An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds
Authors: Zheng Liu, Fu Zhang
Date: 2023-04-29 00:00:00

Bundle adjustment (BA) on LiDAR point clouds has been extensively investigated in recent years due to its ability to optimize multiple poses together, resulting in high accuracy and global consistency for point cloud. However, the accuracy and speed of LiDAR bundle adjustment depend on the quality of plane extraction, which provides point association for LiDAR BA. In this study, we propose a novel and efficient voxel-based approach for plane extraction that is specially designed to provide point association for LiDAR bundle adjustment. To begin, we partition the space into multiple voxels of a fixed size and then split these root voxels based on whether the points are on the same plane, using an octree structure. We also design a novel plane determination method based on principle component analysis (PCA), which segments the points into four even quarters and compare their minimum eigenvalues with that of the initial point cloud. Finally, we adopt a plane merging method to prevent too many small planes from being in a single voxel, which can increase the optimization time required for BA. Our experimental results on HILTI demonstrate that our approach achieves the best precision and least time cost compared to other plane extraction methods.
NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction
Authors: Yijun Yuan, Andreas Nuchter
Date: 2023-04-29 00:00:00
Tasks: 3D Reconstruction

Immersive novel view generation is an important technology in the field of graphics and has recently also received attention for operator-based human-robot interaction. However, the involved training is time-consuming, and thus the current test scope is majorly on object capturing. This limits the usage of related models in the robotics community for 3D reconstruction since robots (1) usually only capture a very small range of view directions to surfaces that cause arbitrary predictions on unseen, novel direction, (2) requires real-time algorithms, and (3) work with growing scenes, e.g., in robotic exploration. The paper proposes a novel Neural Surface Light Fields model that copes with the small range of view directions while producing a good result in unseen directions. Exploiting recent encoding techniques, the training of our model is highly efficient. In addition, we design Multiple Asynchronous Neural Agents (MANA), a universal framework to learn each small region in parallel for large-scale growing scenes. Our model learns online the Neural Surface Light Fields (NSLF) aside from real-time 3D reconstruction with a sequential data stream as the shared input. In addition to online training, our model also provides real-time rendering after completing the data stream for visualization. We implement experiments using well-known RGBD indoor datasets, showing the high flexibility to embed our model into real-time 3D reconstruction and demonstrating high-fidelity view synthesis for these scenes. The code is available on github.
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning
Authors: Fei Wen, Wei Wang, Wenxian Yu
Date: 2023-04-29 00:00:00
Tasks: Denoising, Rain Removal, Super-Resolution

Recent studies show that, without any prior model, the unsupervised restoration learning problem can be optimally formulated as an optimal transport (OT) problem, which has shown promising performance on denoising tasks to approach the performance of supervised methods. However, it still significantly lags behind state-of-the-art supervised methods on complex restoration tasks such as super-resolution, deraining, and dehazing. In this paper, we exploit the sparsity of degradation in the OT framework to significantly boost its performance on these tasks. First, we disclose an observation that the degradation in these tasks is quite sparse in the frequency domain, and then propose a sparsity-aware optimal transport (SOT) criterion for unsupervised restoration learning. Further, we provide an analytic example to illustrate that exploiting the sparsity helps to reduce the ambiguity in finding an inverse map for restoration. Experiments on real-world super-resolution, deraining, and dehazing demonstrate that SOT can improve the PSNR of OT by about 2.6 dB, 2.7 dB and 1.3 dB, respectively, while achieving the best perception scores among the compared supervised and unsupervised methods. Particularly, on the three tasks, SOT significantly outperforms existing unsupervised methods and approaches the performance of state-of-the-art supervised methods.