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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30976 条记录，以下是4651-4660 订阅

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Pose Estimation for Two-View Panoramas based on Keypoint Matching: a Comparative Study and Critical Analysis

Pose Estimation for Two-View Panoramas based on Keypoint Mat...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Murrugarra-Llerena, Jeffri da Silveira, Thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pose estimation is a crucial problem in several computer vision and robotics applications. For the two-view scenario, the typical pipeline consists of finding point correspondences between the two views and using them to estimate the pose. However, most available keypoint extraction and matching methods were designed to work with perspective images and may fail under not-affine distortions present in wide-angle or omnidirectional media, which are becoming increasingly popular in recent years. This paper presents a comprehensive comparative analysis of different keypoint matching algorithms for panoramas coupled to different linear and non-linear approaches for pose estimation. As an additional contribution, we explore a recent approach for mitigating spherical distortions using tangent plane projections, which can be coupled with any planar descriptor, and allows the adaptation of recent learning-based methods. We evaluate the combination of keypoint matching and pose estimation methods using the rotation and translation error of the estimated pose in different scenarios (indoor and outdoor), and our results indicate that SPHORB and "tangent SIFT" are competitive algorithms. We also show that tangent plane adaptations frequently present competitive results, and some optimization steps consistently improve the performance in all methods. We provide code at https://***/Artcs1/Keypoints

关键词： Learning systems Couplings computer vision Planarization Pose estimation Nonlinear distortion Pipelines

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MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Env...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wimbauer, Felix Yang, Nan von Stumberg, Lukas Zeller, Niclas Cremers, Daniel Tech Univ Munich Munich Germany Artisense Palo Alto CA USA

ISBN: (纸本)9781665445092

In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoRec is based on a multi-view stereo setting which encodes the information of multiple consecutive images in a cost volume. lb deal with dynamic objects in the scene, we introduce a MaskModule that predicts moving object masks by leveraging the photometric inconsistencies encoded in the cost volumes. Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks. Furthermore, we present a novel multi-stage training scheme with a semi-supervised loss formulation that does not require LiDAR depth values. We carefully evaluate MonoRec on the KITTI dataset and show that it achieves state-of-the-art performance compared to both multi-view and single-view methods. With the model trained on KITTI, we furthermore demonstrate that MonoRec is able to generalize well to both the Oxford RobotCar dataset and the more challenging TUM-Mono dataset recorded by a handheld camera.

关键词： Training Costs Three-dimensional displays Volume measurement Robot vision systems Prediction methods Lead

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BASNet: Boundary-Aware Salient Object Detection 32

BASNet: Boundary-Aware Salient Object Detection

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qin, Xuebin Zhang, Zichen Huang, Chenyang Gao, Chao Dehghan, Masood Jagersand, Martin Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781728132938

Deep Convolutional Neural Networks have been adopted for salient object detection and achieved the state-of-the-art performance. Most of the previous works however focus on region accuracy but not on the boundary quality. In this paper,we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection. Specifically, the architecture is composed of a densely supervised Encoder-Decoder network and a residual refinement module, which are respectively in charge of saliency prediction and saliency map refinement. The hybrid loss guides the network to learn the transformation between the input image and the ground truth in a three-level hierarchy -pixel-, patch- and map- level- by fusing Binary Cross Entropy (BCE), Structural SIMilarity (SSIM) and Intersection-over-Union (IoU) losses. Equipped with the hybrid loss, the proposed predict-refine architecture is able to effectively segment the salient object regions and accurately predict the fine structures with clear boundaries. Experimental results on six public datasets show that our method outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures. Our method runs at over 25 fps on a single GPU. The code is available at: https://***/NathanUA/BASNet.

关键词： Deep Learning Grouping and Shape Segmentation vision Applications and Systems

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions 32

Rules of the Road: Predicting Driving Behavior with a Convol...

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hong, Joey Sapp, Benjamin Philbin, James CALTECH Pasadena CA 91125 USA Zoox Foster City CA USA

ISBN: (纸本)9781728132938

We focus on the problem of predicting fixture states of entities in complex, real-world driving scenarios. Previous research has used low-level signals to predict short time horizons, and has not addressed how to leverage key assets relied upon heavily by industry self-driving systems: (1) large 3D perception efforts which provide highly accurate 3D states of agents with rich attributes, and (2) detailed and accurate semantic maps of the environment (lanes, traffic lights, crosswalks, etc). We present a unified representation which encodes such high-level semantic information in a spatial grid, allowing the use of deep convolutional models to fuse complex scene context. This enables learning entity-entity and entity-environment interactions with simple, feed-forward computations in each timestep within an overall temporal model of an agent's behavior We propose different ways of modelling the fixture as a distribution over fitture states using standard supervised learning. We introduce a novel dataset providing industry-grade rich perception and semantic inputs, and empirically show we can effectively learn fundamentals of driving behavior

关键词： Deep Learning Motion and Tracking Robotics + Driving vision Applications and Systems

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Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination 32

Natural and Realistic Single Image Super-Resolution with Exp...

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Soh, Jae Woong Park, Gu Yong Jo, Junho Cho, Nam Ik Seoul Natl Univ Dept ECE INMC Seoul South Korea

ISBN: (纸本)9781728132938

Recently, many convolutional neural networksfor single image super-resolution (SISR) have been proposed, which focus on reconstructingthe high-resolutionimages in terms of objective distortion measures. However, the networks trainedwith objective lossfunctions generallyfail to reconstructthe realisticfine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challengingproblem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However the generatedfake details often make undesirableartifacts and the overall image looks somewhat unnatural. Therefore, in thispaper,we present a new approachto reconstructingrealistic super-resolved images with high perceptualquality, while maintainingthe naturalnessof the result. In particular we focus on the domain priorpropertiesof SISR problem. Specifically, we define the naturalnesspriorin the lowlevel domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-orientedones.

关键词： Deep Learning Low-level vision

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SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos 30

SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objec...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Dingwen Yang, Le Meng, Deyu Xu, Dong Han, Junwei Northwestern Polytechincal Univ Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781538604571

Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags. Existing works in this research area might still have some limitations, e.g., lack of effective DNN-based learning frameworks, under-exploring the context information, and requiring to leverage the unstable negative video collection, which prevent them from obtaining more promising performance. To this end, we propose a novel self-paced fine-tuning network (SPFTN)-based framework, which could learn to explore the context information within the video frames and capture adequate object semantics without using the negative videos. To perform weakly supervised learning based on the deep neural network, we make the earliest effort to integrate the self-paced learning regime and the deep neural network into a unified and compatible framework, leading to the self-paced fine-tuning network. Comprehensive experiments on the large-scale YouTube-Objects and DAVIS datasets demonstrate that the proposed approach achieves superior performance as compared with other state-of-the-art methods as well as the baseline networks and models.

关键词： Videos Training Object segmentation Image segmentation computer vision Neural networks Videotapes Object segmentation Neural network images computer vision image segmentation Functional training VIDEO FRAMES

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Real-time self-adaptive deep stereo 32

Real-time self-adaptive deep stereo

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tonioni, Alessio Tosi, Fabio Poggi, Matteo Mattoccia, Stefano di Stefano, Luigi Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9781728132938

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network (MADNet), and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets.

关键词： 3D from Multiview and Sensors Deep Learning Low-level vision

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Multi-Agent Tensor Fusion for Contextual Trajectory Prediction 32

Multi-Agent Tensor Fusion for Contextual Trajectory Predicti...

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Tianyang Xu, Yifei Monfort, Mathew Choi, Wongun Baker, Chris Zhao, Yibiao Wang, Yizhou Wu, Ying Nian ISEE AI Cambridge MA USA Peking Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA 90024 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781728132938

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.

关键词： Deep Learning Motion and Tracking Robotics + Driving Scene Analysis and Understanding vision Applications and System

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Grounding Human-to-Vehicle Advice for Self-driving Vehicles 32

Grounding Human-to-Vehicle Advice for Self-driving Vehicles

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim, Jinkyu Misu, Teruhisa Chen, Yi-Ting Tawari, Ashish Canny, John Univ Calif Berkeley EECS Berkeley CA 94720 USA Honda Res Inst USA Inc San Jose CA USA

ISBN: (纸本)9781728132938

Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human actions, but they lack semantic understanding of image contents. This makes them brittle and potentially unsafe in situations that do not match training data. Here, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present a first step toward advice giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Attention mechanisms tie controller behavior to salient objects in the advice. We evaluate our model on a novel advisable driving dataset with manually annotated human-to-vehicle advice called Honda Research Institute-Advice Dataset (HAD). We show that taking advice improves the performance of the end-to-end network, while the network cues on a variety of visual features that are provided by advice. The dataset is available at https://***/HAD.

关键词： Deep Learning Robotics + Driving vision + Language

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