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arXiv 2022年

作者： Storath, Martin Weinmann, Andreas Lab for Mathematical Methods in Computer Vision and Machine Learning Technische Hochschule Würzburg-Schweinfurt Schweinfurt Germany Department of Mathematics and Natural Sciences Hochschule Darmstadt Darmstadt Germany

Smoothing splines are twice differentiable by construction, so they cannot capture potential discontinuities in the underlying signal. In this work, we consider a special case of the weak rod model of Blake and Zisserman (1987) that allows for discontinuities penalizing their number by a linear term. The corresponding estimates are cubic smoothing splines with discontinuities (CSSD) which serve as representations of piecewise smooth signals and facilitate exploratory data analysis. However, computing the estimates requires solving a non-convex optimization problem. So far, efficient and exact solvers exist only for a discrete approximation based on equidistantly sampled data. In this work, we propose an efficient solver for the continuous minimization problem with non-equidistantly sampled data. Its worst case complexity is quadratic in the number of data points, and if the number of detected discontinuities scales linearly with the signal length, we observe linear growth in runtime. This efficient algorithm allows to use cross validation for automatic selection of the hyperparameters within a reasonable time frame on standard hardware. We provide a reference implementation and supplementary material. We demonstrate the applicability of the approach for the aforementioned tasks using both simulated and real *** Codes 65D07, 65K10, 62G08 Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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learning to segment with image-level annotations

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Pattern Recognition 2016年 59卷 234-244页

作者： Wei, Yunchao Liang, Xiaodan Chen, Yunpeng Jie, Zequn Xiao, Yanhui Zhao, Yao Yan, Shuicheng Institute of information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China School of Advanced Computing Sun Yat-Sen University Guangzhou 510006 China Vision and Machine Learning Lab National University of Singapore 117583 Singapore People׳s Public Security University of China Beijing 100038 China

Recently, deep convolutional neural networks (DCNNs) have significantly promoted the development of semantic image segmentation. However, previous works on learning the segmentation network often rely on a large number of ground-truths with pixel-level annotations, which usually require considerable human effort. In this paper, we explore a more challenging problem by learning to segment under image-level annotations. Specifically, our framework consists of two components. First, reliable hypotheses based localization maps are generated by incorporating the hypotheses-aware classification and cross-image contextual refinement. Second, the segmentation network can be trained in a supervised manner by these generated localization maps. We explore two network training strategies for achieving good segmentation performance. For the first strategy, a novel multi-label cross-entropy loss is proposed to train the network by directly using multiple localization maps for all classes, where each pixel contributes to each class with different weights. For the second strategy, the rough segmentation mask can be inferred from the localization maps, and then the network is optimized based on the single-label cross-entropy loss with the produced masks. We evaluate our methods on the PASCAL VOC 2012 segmentation benchmark. Extensive experimental results demonstrate the effectiveness of the proposed methods compared with the state-of-the-arts. © 2016 Elsevier Ltd

关键词： Deep learning Semantic segmentation Weakly supervised

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learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

Learning a Shape-Conditioned Agent for Purely Tactile In-Han...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Johannes Pitz Lennart Röstel Leon Sievers Darius Burschka Berthold Bäuml Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany DLR Institute of Robotics & Mechatronics (German Aerospace Center) Machine Vision and Perception Group Technical University of Munich

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ~90% even for non-convex ***: https://***/manipulation/iros24

关键词： Training Visualization Three-dimensional displays Torque Shape Tactile sensors Reinforcement learning Position measurement Vectors Sensors

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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Generalized label enhancement with sample correlations

arXiv

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Tang, Haoyu Liu, Xinyuan Li, Zhongyu Lu, Huimin Lab of Vision Computing and Machine Learning School of Software Engineering Xi'an Jiaotong University Xi'an710049 China Environment Recognition & Intelligent Computation Laboratory Kyushu Institute of Technology Japan

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., label Enhancement with Sample Correlations (LESC) and generalized label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Tensor-based Intrinsic Subspace Representation learning for Multi-view Clustering

arXiv

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arXiv 2020年

作者： Zheng, Qinghai Zhang, Yu Zhu, Jihua Li, Zhongyu Tang, Haoyu Ma, Shuangxun Lab of Vision Computing and Machine Learning School of Software Engineering Xi'an Jiaotong University Xi'An710049 China School of Software Engineering Xi'an Jiaotong University Xi'An710049 China

As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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RangeBird: Multi View Panoptic Segmentation of 3D Point Clouds with Neighborhood Attention

RangeBird: Multi View Panoptic Segmentation of 3D Point Clou...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Fabian Duerr Hendrik Weigel Jü rgen Beyerer Audi AG Ingolstadt Germany Vision and Fusion Lab Karlsruhe Institute of Technology Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation Fraunhofer Center for Machine Learning Karlsruhe Germany

Panoptic segmentation of point clouds is one of the key challenges of 3D scene understanding, requiring the simultaneous prediction of semantics and object instances. Tasks like autonomous driving strongly depend on these information to get a holistic understanding of their 3D environment. This work presents a novel proposal free framework for lidar-based panoptic segmentation, which exploits three different point cloud representations, leveraging their strengths and compensating their weaknesses. The efficient projection-based range view and bird's eye view are combined and further extended by a point-based network with a novel attention-based neighborhood aggregation for improved semantic features. Cluster-based object recognition in bird's eye view enables an efficient and high-quality instance segmentation. Semantic and instance segmentation are fused and further refined by a novel instance classification for the final panoptic segmentation. The results on two challenging large-scale datasets, nuScenes and SemanticKITTI, show the success of the proposed framework, which outperforms all existing approaches on nuScenes and achieves state-of-the-art results on SemanticKITTI.

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FUSIONvision: A COMPREHENSIVE APPROACH OF 3D OBJECT RECONSTRUCTION AND SEGMENTATION FROM RGB-D CAMERAS USING YOLO AND FAST SEGMENT ANYTHING

arXiv

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arXiv 2024年

作者： El Ghazouali, Safouane Mhirit, Youssef Oukhrid, Ali Michelucci, Umberto Nouira, Hichem TOELT LLC - Computer Vision Machine learning Lab Winterthur Switzerland Paris France Sonceboz Switzerland LNE - Laboratoire National de & Métrologie et d'Essais Paris France

In the realm of computer vision, the integration of advanced techniques into the preprocessing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces Fusionvision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth map as they are mainly proposed for RGB cameras. To address this challenge, Fusionvision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed Fusionvision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation. The code and pre-trained models are publicly available at https://***/safouaneelg/Fusionvision/ (accessed on 28 February 2024). © 2024, CC BY-NC-SA.

关键词： Object detection

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Video Test-Time Adaptation for Action Recognition

Video Test-Time Adaptation for Action Recognition

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Conference on Computer vision and Pattern Recognition (CVPR)

作者： Wei Lin Muhammad Jehanzeb Mirza Mateusz Kozinski Horst Possegger Hilde Kuehne Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Christian Doppler Laboratory for Embedded Machine Learning Goethe University Frankfurt Germany MIT-IBM Watson AI Lab

Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. Code will be available at https://***/wlin-at/ViTTA.

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Sit Back and Relax: learning to Drive Incrementally in All Weather Conditions

Sit Back and Relax: Learning to Drive Incrementally in All W...

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IEEE Symposium on Intelligent Vehicle

作者： Stefan Leitner M. Jehanzeb Mirza Wei Lin Jakub Micorek Marc Masana Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Christian Doppler Laboratory for Semantic 3D Computer Vision Silicon Austria Labs TU Graz - SAL Dependable Embedded Systems Lab

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.

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