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A Structured Dictionary Perspective on Implicit Neural Representations

A Structured Dictionary Perspective on Implicit Neural Repre...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuce, Gizem Ortiz-Jimenez, Guillermo Besbinar, Beril Frossard, Pascal Swiss Fed Inst Technol Zurich Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a novel unified perspective to theoretically analyse INRs. Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies. This structure allows INRs to express signals with an exponentially increasing frequency support using a number of parameters that only grows linearly with depth. We also explore the inductive bias of INRs exploiting recent results about the empirical neural tangent kernel (NTK). Specifically, we show that the eigenfunctions of the NTK can be seen as dictionary atoms whose inner product with the target signal determines the final performance of their reconstruction. In this regard, we reveal that meta-learning has a reshaping effect on the NTK analogous to dictionary learning, building dictionary atoms as a combination of the examples seen during meta-training. Our results permit to design and tune novel INR architectures, but can also be of interest for the wider deep learning theory community.

关键词： Deep learning Computer vision Dictionaries Data visualization Power system harmonics Harmonic analysis Eigenvalues and eigenfunctions

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Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation

Zero Experience Required: Plug & Play Modular Transfer Learn...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Al-Halah, Ziad Ramakrishnan, Santhosh K. Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In reinforcement learning for visual navigation, it is common to develop a model for each new task, and train that model from scratch with task-specific interactions in 31) environments. However, this process is expensive;massive amounts of interactions are needed for the model to generalize well. Moreover;this process is repeated whenever there is a change in the task type or the goal modality. We present a unified approach to visual navigation using a novel modular transfer learning model. Our model can effectively leverage its experience from one source task and apply it to multiple target tasks (e.g., ObjectNav, RoomNay, ViewNav) with various goal modalities (e.g., image, sketch, audio, label). Furthermore, our model enables zeroshot experience learning, whereby it can solve the target tasks without receiving any task-specific interactive training. Our experiments on multiple photorealistic datasets and challenging tasks show that our approach learns faster;generalizes better, and outperforms SoTA models by a significant margin. Project page: https://***/projects/zsel/

关键词： Training visualization Solid modeling Three-dimensional displays Navigation Semantic search Transfer learning

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CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Pr...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Hu, Xinggang Zhang, Yunzhou Cao, Zhenzhong Ma, Rong Wu, Yanmin Deng, Zhiqiang Sun, Wenkai Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Beijing Simulat Ctr Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.

关键词： Location awareness visualization Simultaneous localization and mapping Heuristic algorithms Semantics Pose estimation Clustering algorithms

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Navigation Among Movable Obstacles with Object Localization using Photorealistic Simulation

Navigation Among Movable Obstacles with Object Localization ...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Ellis, Kirsty Zhang, Henry Stoyanov, Danail Kanoulas, Dimitrios UCL Dept Comp Sci Gower St London WC1E 6BT England

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

While mobile navigation has been focused on obstacle avoidance, Navigation Among Movable Obstacles (NAMO) via interaction with the environment, is a problem that is still open and challenging. This paper, presents a novel system integration to handle NAMO using visual feedback. In order to explore the capabilities of our introduced system, we explore the solution of the problem via graph-based path planning in a photorealistic simulator (NVIDIA Isaac Sim), in order to identify if the simulation-to-reality (sim2real) problem in robot navigation can be resolved. We consider the case where a wheeled robot navigates in a warehouse, in which movable boxes are common obstacles. We enable online real-time object localization and obstacle movability detection, to either avoid objects or, if it is not possible, to clear them out from the robot planned path by using pushing actions. We firstly test the integrated system in photorealistic environments, and we then validate the method on a real-world mobile wheeled robot (UCL MPPL) and its on-board sensory and computing system.

关键词： Location awareness visualization Navigation Computational modeling System integration Robot sensing systems Real-time systems

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NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

NightLab: A Dual-level Architecture with Hardness Detection ...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Deng, Xueqing Wang, Peng Lian, Xiaochen Newsam, Shawn Univ Calif Merced EECS Merced CA 95343 USA ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared to its daytime counterpart. In this paper, we propose NightLab, a novel nighttime segmentation framework that leverages multiple deep learning models imbued with night-aware features to yield State-of-The-Art (SoTA) performance on multiple night segmentation benchmarks. Notably, Night Lab contains models at two levels of granularity, i.e. image and regional, and each level is composed of light adaptation and segmentation modules. Given a nighttime image, the image level model provides an initial segmentation estimate while, in parallel, a hardness detection module identifies regions and their surrounding context that need further analysis. A regional level model focuses on these difficult regions to provide a significantly improved segmentation. All the models in NightLab are trained end-to-end using a set of proposed night-aware losses without handcrafted heuristics. Extensive experiments on the NightCity [44] and BDDIOOK [59] datasets show Night Lab achieves SoTA performance compared to concurrent methods. Code and dataset are available at https://***/xdeng7/NightLab.

关键词： Deep learning Image segmentation Adaptation models visualization Computer vision Image analysis Semantics

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Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization

Exact Feature Distribution Matching for Arbitrary Style Tran...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Yabin Li, Minghan Li, Ruihuang Jia, Kui Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature distribution, conventional feature distribution matching methods usually match the mean and standard deviation of features. However, the feature distributions of real-world data are usually much more complicated than Gaussian, which cannot be accurately matched by using only the first-order and second-order statistics, while it is computationally prohibitive to use high-order statistics for distribution matching. In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play manner with minimal cost. The effectiveness of our proposed EFDM method is verified on a variety of AST and DG tasks, demonstrating new state-of-the-art results. Codes are available at https://***/YBZh/EFDM.

关键词： visualization Histograms Computer vision Image recognition Costs Statistical analysis Pattern recognition

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Automated Aerial Screwing with a Fully Actuated Aerial Manipulator

Automated Aerial Screwing with a Fully Actuated Aerial Manip...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Schuster, Micha Bernstein, David Reck, Paul Hamaza, Salua Beitelschmidt, Michael Tech Univ Dresden TU Dresden Chair Dynam & Mech Design Dresden Germany Delft Univ Technol TU Delft Micro Air Vehicle Lab Delft Netherlands

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The tasks that unmanned aerial vehicles (UAVs) have taken upon have progressively grown in complexity over the years, alongside with the level of autonomy with which they are carried out. In this work, we present an example of aerial screwing operations with a fully-actuated tilt-rotor platform. Key contributions include a new control framework to automate screwing operations through a robust hole search and in-hole detection algorithm. These are achieved without a-priori knowledge of the exact hole location, and without the use of external tools, such as vision based hole detection or force sensors. Wrench coupling is implemented to account for the platform's kinematic constraints during screwing. The application of a constant contact force and a compliant response to induced disturbances are obtained with the use of admittance control. The full framework is validated with extensive flight experiments that demonstrate the effectiveness of each subsystem, as well as the complete architecture. We also validate the robustness of the detection algorithm against false positives. Within the results we demonstrate the ability to perform the automated task with a 86% success rate over 35 flights, and measured hole search time of 9s (median value).

关键词： visualization Three-dimensional displays Force Pipelines Robot sensing systems Robustness Time measurement

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Text2Mesh: Text-Driven Neural Stylization for Meshes

Text2Mesh: Text-Driven Neural Stylization for Meshes

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Michel, Oscar Bar-On, Roi Liu, Richard Benaim, Sagie Hanocka, Rana Univ Chicago Chicago IL 60637 USA Tel Aviv Univ Tel Aviv Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term a neural style field network (NSF). In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization. We demonstrate the ability of our technique to synthesize a myriad of styles over a wide variety of 3D meshes. Our code and results are available in our project webpage: https://***/text2mesh/.

关键词： Solid modeling visualization Three-dimensional displays Image color analysis Shape Neural networks Topology

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Collaborative Transformers for Grounded Situation Recognition

Collaborative Transformers for Grounded Situation Recognitio...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cho, Junhyeong Yoon, Youngseok Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Grounded situation recognition is the task of predicting the main activity, entities playing certain roles within the activity, and bounding-box groundings of the entities in the given image. To effectively deal with this challenging task, we introduce a novel approach where the two processes for activity classification and entity estimation are interactive and complementary. To implement this idea, we propose Collaborative Glance-Gaze TransFormer (CoFormer) that consists of two modules: Glance transformer for activity classification and Gaze transformer for entity estimation. Glance transformer predicts the main activity with the help of Gaze transformer that analyzes entities and their relations, while Gaze transformer estimates the grounded entities by focusing only on the entities relevant to the activity predicted by Glance transformer. Our CoFormer achieves the state of the art in all evaluation metrics on the SWiG dataset. Training code and model weights are available at htpps://***/jhcho99/CoFormer.

关键词： Measurement Training visualization Computational modeling Estimation Collaboration Predictive models

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VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM 39

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial...

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ieee International conference on Robotics and Automation (ICRA)

作者： Chen, Danpeng Wang, Shuai Xie, Weijian Zhai, Shangjin Wang, Nan Bao, Hujun Zhang, Guofeng Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China SenseTime Res Hong Kong Peoples R China Tetras AI Singapore Singapore

ISBN: (纸本)9781728196817

In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight back-end map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively improve the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.

关键词： Bundle adjustment visualization Simultaneous localization and mapping Feature extraction Sensor systems Mobile handsets Data models

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