pwc-archive/papers-with-abstracts huggingface dataset:
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Authors: Lei Qi, Jiaying Shen, Jiaqi Liu, Yinghuan Shi, Xin Geng
Date: 2022-04-12 00:00:00
Tasks: Person Re-Identification

Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficult to directly apply the supervised model to arbitrary unseen domains due to the domain gap between the available source domains and unseen target domains. In this paper, we propose a novel label distribution learning (LDL) method to address the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training), which aims to explore the relation of different classes and mitigate the domain-shift across different domains so as to improve the discrimination of the model and learn the domain-invariant feature, simultaneously. Specifically, during the training process, we produce the label distribution via the online manner to mine the relation information of different classes, thus it is beneficial for extracting the discriminative feature. Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature. Furthermore, we also give the theoretical analysis to demonstrate that the proposed method can effectively deal with the domain-shift issue. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
Authors: Seongmin Lee, Zijie J. Wang, Judy Hoffman, Duen Horng Chau
Date: 2022-04-12 00:00:00
Proceeding: CVPR 2022 1
Tasks: image-classification, Image Classification

CNN image classifiers are widely used, thanks to their efficiency and accuracy. However, they can suffer from biases that impede their practical applications. Most existing bias investigation techniques are either inapplicable to general image classification tasks or require significant user efforts in perusing all data subgroups to manually specify which data attributes to inspect. We present VisCUIT, an interactive visualization system that reveals how and why a CNN classifier is biased. VisCUIT visually summarizes the subgroups on which the classifier underperforms and helps users discover and characterize the cause of the underperformances by revealing image concepts responsible for activating neurons that contribute to misclassifications. VisCUIT runs in modern browsers and is open-source, allowing people to easily access and extend the tool to other model architectures and datasets. VisCUIT is available at the following public demo link: https://poloclub.github.io/VisCUIT. A video demo is available at https://youtu.be/eNDbSyM4R_4.
Exploring Event Camera-based Odometry for Planetary Robots
Authors: Florian Mahlknecht, Daniel Gehrig, Jeremy Nash, Friedrich M. Rockenbauer, Benjamin Morrell, Jeff Delaune, Davide Scaramuzza
Date: 2022-04-12 00:00:00

Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle, since they cannot cope with significant depth uncertainties caused by an unforeseen loss of tracking or other effects. In this work, we introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based frontend with a filter-based backend. This makes it both accurate and robust to uncertainties, outperforming event- and frame-based VIO algorithms on challenging benchmarks by 32%. In addition, we demonstrate accurate performance in hover-like conditions (outperforming existing event-based methods) as well as high robustness in newly collected Mars-like and high-dynamic-range sequences, where existing frame-based methods fail. In doing so, we show that event-based VIO is the way forward for vision-based exploration on Mars.
Semantic keypoint-based pose estimation from single RGB frames
Authors: Karl Schmeckpeper, Philip R. Osteen, Yufu Wang, Georgios Pavlakos, Kenneth Chaney, Wyatt Jordan, Xiaowei Zhou, Konstantinos G. Derpanis, Kostas Daniilidis
Date: 2022-04-12 00:00:00
Tasks: Object, Pose Estimation

This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior investigators, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training-image data. Furthermore, the approach can be applied to instance- and class-based pose recovery. Additionally, we accompany our main pipeline with a technique for semi-automatic data generation from unlabeled videos. This procedure allows us to train the learnable components of our method with minimal manual intervention in the labeling process. Empirically, we show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios even against a cluttered background. We apply our approach both to several, existing, large-scale datasets - including PASCAL3D+, LineMOD-Occluded, YCB-Video, and TUD-Light - and, using our labeling pipeline, to a new dataset with novel object classes that we introduce here. Extensive empirical evaluations show that our approach is able to provide pose estimation results comparable to the state of the art.
Bootstrap Motion Forecasting With Self-Consistent Constraints
Authors: Maosheng Ye, Jiamiao Xu, Xunnong Xu, Tengfei Wang, Tongyi Cao, Qifeng Chen
Date: 2022-04-12 00:00:00
Proceeding: ICCV 2023 1
Tasks: Motion Forecasting

We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of MISC is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during training. Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision. With explicit constraints from multiple teacher targets, we observe a clear improvement in the prediction performance. Extensive experiments on the Argoverse motion forecasting benchmark and Waymo Open Motion dataset show that MISC significantly outperforms the state-of-the-art methods. As the proposed strategies are general and can be easily incorporated into other motion forecasting approaches, we also demonstrate that our proposed scheme consistently improves the prediction performance of several existing methods.
Probabilistic Compositional Embeddings for Multimodal Image Retrieval
Authors: Andrei Neculai, Yanbei Chen, Zeynep Akata
Date: 2022-04-12 00:00:00
Tasks: Image Retrieval, Retrieval

Existing works in image retrieval often consider retrieving images with one or two query inputs, which do not generalize to multiple queries. In this work, we investigate a more challenging scenario for composing multiple multimodal queries in image retrieval. Given an arbitrary number of query images and (or) texts, our goal is to retrieve target images containing the semantic concepts specified in multiple multimodal queries. To learn an informative embedding that can flexibly encode the semantics of various queries, we propose a novel multimodal probabilistic composer (MPC). Specifically, we model input images and texts as probabilistic embeddings, which can be further composed by a probabilistic composition rule to facilitate image retrieval with multiple multimodal queries. We propose a new benchmark based on the MS-COCO dataset and evaluate our model on various setups that compose multiple images and (or) text queries for multimodal image retrieval. Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities. Code is available here: https://github.com/andreineculai/MPC.
Generative Negative Replay for Continual Learning
Authors: Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo Lomonaco
Date: 2022-04-12 00:00:00
Tasks: Continual Learning

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetting, the Achilles' heel of continual learning, is storing part of the old data and replaying them interleaved with new experiences (also known as the replay approach). Generative replay, which is using generative models to provide replay patterns on demand, is particularly intriguing, however, it was shown to be effective mainly under simplified assumptions, such as simple scenarios and low-dimensional data. In this paper, we show that, while the generated data are usually not able to improve the classification accuracy for the old classes, they can be effective as negative examples (or antagonists) to better learn the new classes, especially when the learning experiences are small and contain examples of just one or few classes. The proposed approach is validated on complex class-incremental and data-incremental continual learning scenarios (CORe50 and ImageNet-1000) composed of high-dimensional data and a large number of training experiences: a setup where existing generative replay approaches usually fail.
EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data
Authors: Anastasiia Kornilova, Dmitrii Iarosh, Denis Kukushkin, Nikolai Goncharov, Pavel Mokeev, Arthur Saliou, Gonzalo Ferrer
Date: 2022-04-12 00:00:00
Tasks: Benchmarking, Segmentation

This paper provides the EVOPS dataset for plane segmentation from 3D data, both from RGBD images and LiDAR point clouds. We have designed two annotation methodologies (RGBD and LiDAR) running on well-known and widely-used datasets for SLAM evaluation and we have provided a complete set of benchmarking tools including point, planes and segmentation metrics. The data includes a total number of 10k RGBD and 7K LiDAR frames over different selected scenes which consist of high quality segmented planes. The experiments report quality of SOTA methods for RGBD plane segmentation on our annotated data. We also have provided learnable baseline for plane segmentation in LiDAR point clouds. All labeled data and benchmark tools used have been made publicly available at https://evops.netlify.app/.
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data
Authors: Finn Behrendt, Marcel Bengs, Frederik Rogge, Julia Krüger, Roland Opfer, Alexander Schlaefer
Date: 2022-04-12 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our evaluations, we consider three publicly available data sets and use autoencoders (AE) as a well-established baseline method for UAD. We systematically evaluate the effect of impured training data by injecting different quantities of unhealthy samples to our training set of healthy samples from T1-weighted MRI-scans. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE. Our results show that training with impured data decreases the UAD performance notably even with few falsely labeled samples. By performing outlier removal directly during training based on the reconstruction-loss, we demonstrate that falsely labeled data can be detected and removed to mitigate the effect of falsely labeled data. Overall, we highlight the importance of clean data sets for UAD in brain MRI and demonstrate an approach for detecting falsely labeled data directly during training.
GORDA: Graph-based ORientation Distribution Analysis of SLI scatterometry Patterns of Nerve Fibres
Authors: Esteban Vaca, Miriam Menzel, Katrin Amunts, Markus Axer, Timo Dickscheid
Date: 2022-04-12 00:00:00

Scattered Light Imaging (SLI) is a novel approach for microscopically revealing the fibre architecture of unstained brain sections. The measurements are obtained by illuminating brain sections from different angles and measuring the transmitted (scattered) light under normal incidence. The evaluation of scattering profiles commonly relies on a peak picking technique and feature extraction from the peaks, which allows quantitative determination of parallel and crossing in-plane nerve fibre directions for each image pixel. However, the estimation of the 3D orientation of the fibres cannot be assessed with the traditional methodology. We propose an unsupervised learning approach using spherical convolutions for estimating the 3D orientation of neural fibres, resulting in a more detailed interpretation of the fibre orientation distributions in the brain.
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks
Authors: Štefan Pócoš, Iveta Bečková, Igor Farkaš
Date: 2022-04-12 00:00:00
Tasks: image-classification, Image Classification

Deep neural networks achieve remarkable performance in multiple fields. However, after proper training they suffer from an inherent vulnerability against adversarial examples (AEs). In this work we shed light on inner representations of the AEs by analysing their activations on the hidden layers. We test various types of AEs, each crafted using a specific norm constraint, which affects their visual appearance and eventually their behavior in the trained networks. Our results in image classification tasks (MNIST and CIFAR-10) reveal qualitative differences between the individual types of AEs, when comparing their proximity to the class-specific manifolds on the inner representations. We propose two methods that can be used to compare the distances to class-specific manifolds, regardless of the changing dimensions throughout the network. Using these methods, we consistently confirm that some of the adversarials do not necessarily leave the proximity of the manifold of the correct class, not even in the last hidden layer of the neural network. Next, using UMAP visualisation technique, we project the class activations to 2D space. The results indicate that the activations of the individual AEs are entangled with the activations of the test set. This, however, does not hold for a group of crafted inputs called the rubbish class. We also confirm the entanglement of adversarials with the test set numerically using the soft nearest neighbour loss.
LifeLonger: A Benchmark for Continual Disease Classification
Authors: Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring, Cees G. M. Snoek
Date: 2022-04-12 00:00:00
Tasks: Classification, class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark will be made publicly available.
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
Authors: Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, Simon Lucey
Date: 2022-04-12 00:00:00
Tasks: NeRF, Novel View Synthesis, Pose Estimation

Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses. Although approaches for jointly recovering the radiance field and camera pose exist (BARF), they rely on a cumbersome coarse-to-fine auxiliary positional embedding to ensure good performance. We present Gaussian Activated neural Radiance Fields (GARF), a new positional embedding-free neural radiance field architecture - employing Gaussian activations - that outperforms the current state-of-the-art in terms of high fidelity reconstruction and pose estimation.
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
Authors: José A. Padrón-Hidalgo, Valero Laparra, Gustau Camps-Valls
Date: 2022-04-12 00:00:00
Tasks: Anomaly Detection, Change Detection

Anomaly detection is a field of intense research. Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary. We propose an unsupervised method for detecting anomalies and changes in remote sensing images by means of a multivariate Gaussianization methodology that allows to estimate multivariate densities accurately, a long-standing problem in statistics and machine learning. The methodology transforms arbitrarily complex multivariate data into a multivariate Gaussian distribution. Since the transformation is differentiable, by applying the change of variables formula one can estimate the probability at any point of the original domain. The assumption is straightforward: pixels with low estimated probability are considered anomalies. Our method can describe any multivariate distribution, makes an efficient use of memory and computational resources, and is parameter-free. We show the efficiency of the method in experiments involving both anomaly detection and change detection in different remote sensing image sets. Results show that our approach outperforms other linear and nonlinear methods in terms of detection power in both anomaly and change detection scenarios, showing robustness and scalability to dimensionality and sample sizes.
Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris
Authors: Fernando Alonso-Fernandez, Reuben A. Farrugia, Julian Fierrez, Josef Bigun
Date: 2022-04-12 00:00:00
Tasks: Super-Resolution

The lack of resolution has a negative impact on the performance of image-based biometrics. Many applications which are becoming ubiquitous in mobile devices do not operate in a controlled environment, and their performance significantly drops due to the lack of pixel resolution. While many generic super-resolution techniques have been studied to restore low-resolution images for biometrics, the results obtained are not always as desired. Those generic methods are usually aimed to enhance the visual appearance of the scene. However, producing an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Such techniques are designed to restore generic images and therefore do not exploit the specific structure found in biometric images (e.g. iris or faces), which causes the solution to be sub-optimal. For this reason, super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. In recent years, there has been an increased interest in the application of super-resolution to different biometric modalities, such as face iris, gait or fingerprint. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images, which are the two prevalent modalities in selfie biometrics. We also provide experimental results using several state-of-the-art reconstruction algorithms, demonstrating the benefits of using super-resolution to improve the quality of face and iris images prior to classification. In the reported experiments, we study the application of super-resolution to face and iris images captured in the visible range, using experimental setups that represent well the selfie biometrics scenario.
3DeformRS: Certifying Spatial Deformations on Point Clouds
Authors: Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra, Silvio Giancola, Bernard Ghanem
Date: 2022-04-12 00:00:00
Proceeding: CVPR 2022 1
Tasks: Autonomous Driving

3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these models against real-world deformations must be addressed practically and reliably. In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations. We developed 3DeformRS by building upon recent work that generalized Randomized Smoothing (RS) from pixel-intensity perturbations to vector-field deformations. In particular, we specialized RS to certify DNNs against parameterized deformations (e.g. rotation, twisting), while enjoying practical computational costs. We leverage the virtues of 3DeformRS to conduct a comprehensive empirical study on the certified robustness of four representative point cloud DNNs on two datasets and against seven different deformations. Compared to previous approaches for certifying point cloud DNNs, 3DeformRS is fast, scales well with point cloud size, and provides comparable-to-better certificates. For instance, when certifying a plain PointNet against a 3{\deg} z-rotation on 1024-point clouds, 3DeformRS grants a certificate 3x larger and 20x faster than previous work.