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Learning Surface Terrain Classifications from Ground Penetrating Radar

Learning Surface Terrain Classifications from Ground Penetra...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anja Sheppard Jason Brown Nilton Renno Katherine A. Skinner The University of Michigan Ann Arbor MI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR’s existing scientific use cases.

关键词： Ground penetrating radar Surface waves Semantics Robot vision systems Sensor systems Sensors Planning

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Towards Real-Time 4K Image Super-Resolution

Towards Real-Time 4K Image Super-Resolution

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eduard Zamfir Marcos V. Conde Radu Timofte Computer Vision Lab CAIDAS IFI University of Würzburg Germany

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard. While higher resolutions offer improved visual quality for users, they pose a significant challenge for super-resolution networks to achieve real-time performance on commercial GPUs. This paper presents a comprehensive analysis of super-resolution model designs and techniques aimed at efficiently upscaling images from 720p and 1080p resolutions to 4K. We begin with a simple, effective baseline architecture and gradually modify its design by focusing on extracting important high-frequency details efficiently. This allows us to subsequently downscale the resolution of deep feature maps, reducing the overall computational footprint, while maintaining high reconstruction fidelity. We enhance our method by incorporating pixel-unshuffling, a simplified and speed-up reinterpretation of the basic block proposed by NAFNet, along with structural re-parameterization. We assess the performance of the fastest version of our method in the new NTIRE 2023 Real-Time 4K Super-Resolution challenge and demonstrate its potential in comparison with state-of-the-art efficient super-resolution models when scaled up. Our method was tested successfully on high-quality content from photography, digital art, and gaming content.

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Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

Training Transformer Models by Wavelet Losses Improves Quant...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Cansu Korkmaz A. Murat Tekalp College of Engineering and KUIS AI Center Koc University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as l 1 , have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

关键词： Wavelet transforms Training Visualization Computational modeling conferences Superresolution computer architecture

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection

DaFF: Dual Attentive Feature Fusion for Multispectral Pedest...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Afnan Althoupety Li-Yun Wang Wu-Chi Feng Banafsheh Rekabdar Portland State University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inspired by how humans perceive and interpret the world using multiple senses, multi-modal learning involves integrating information from multiple modalities to improve understanding and performance in various tasks. Aligning with that notion, our key intuition is to utilize multi-model learning to solve the domain shift problem in night-time pedestrian *** this paper, we show that pairing RGB and infrared (IR) image features increases the robustness of pedestrian detection at night. Indeed, this solution is unbiased towards a specific time of the day as the IR domain reduces the reliance on lighting and serves as complementary information to the RGB domain. Our work aims at exploiting the power of attention mechanisms to guide a multi-modal framework in feature fusing from RGB and IR modalities. Our novel fusion approach, named dual attentive feature fusion (DaFF), leverages the duality of the transformer and channel-wise global attentions. To demonstrate the effectiveness of DaFF, we conducted experiments on two real-world multispectral pedestrian datasets. Extensive experimental results reveal the superiority of DaFF. We believe that combining the complementary properties of RGB and IR modalities is an effective remedy to mitigate the domain shift problem in pedestrian detection.

关键词： Degradation computer vision Pedestrians Attention mechanisms conferences Lighting Feature extraction

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SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes

SemiGPC: Distribution-Aware Label Refinement for Imbalanced ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Abdelhak Lemkhenter Manchen Wang Luca Zancato Gurumurthy Swaminathan Paolo Favaro Davide Modolo AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as Co-Match [17] and SimMatch [34], our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity. This explicit control allows SemiGPC to be more robust to confirmation bias especially under class imbalance. We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch [23], ReMixMatch [4], SimMatch [34] and FreeMatch [32] and different pre-training strategies including MSN [2] and Dino [5]. We also show that SemiGPC achieves state of the art results under different degrees of class imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime. Using SemiGPC also results in about 2% avg. accuracy increase compared to a new competitive baseline on the more challenging benchmarks SemiAves, SemiCUB, SemiFungi [27] and Semi-iNat [26].

关键词： computer vision Sensitivity Accuracy conferences Gaussian processes Semisupervised learning Predictive models

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tonmoay Deb Lichen Wang Zachary Bessinger Naji Khosravan Eric Penner Sing Bing Kang Northwestern University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper focuses on bridging the gap between natural language descriptions, 360° panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell 1 . We first introduce an effective technique for extracting geometric information from ZInD’s raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naïve methods in both tasks, hence, can be considered as a complement to the naïve to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： Geometry computer vision Shape Focusing Benchmark testing Information retrieval Human in the loop

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On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

On the Real-Time Semantic Segmentation of Aphid Clusters in ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Raiyan Rahman Christopher Indris Tianxiao Zhang Kaidong Li Brian McCornack Daniel Flippo Ajay Sharda Guanghui Wang Department of Computer Science Toronto Metropolitan University Toronto Canada Department of Electrical Engineering and Computer Science University of Kansas Lawrence USA Department of Entomology Kansas State University Manhattan USA Department of Biological and Agricultural Engineering Kansas State University Manhattan USA

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.

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A System for Dense Monocular Mapping with a Fisheye Camera

A System for Dense Monocular Mapping with a Fisheye Camera

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Louis Gallagher Ganesh Sistu Jonathan Horgan John B. McDonald Department of Computer Science Maynooth University Co Kildare Ireland Valeo Vision Systems Tuam Ireland

We introduce a novel dense mapping system that uses a single monocular fisheye camera as the sole input sensor and incrementally builds a dense surfel representations of the scene’s 3D geometry. We extend an existing hybrid sparse-dense monocular SLAM system, reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. Each frame is processed in its original undistorted fisheye form, with no attempt to remove distortion. To estimate depth, we introduce a new version of the PackNet depth estimation neural network adapted for fisheye inputs. We reformulate PackNet’s multi-view stereo self-supervised loss in terms of the Kannala-Brandt fisheye camera model. To encourage the network to learn metric depth during training, the pose network is weakly supervised with the camera’s ground-truth inter-frame velocity. To improve overall performance, we additionally provide sparse depth supervision from dataset LiDAR and SICK laser scanners. We demonstrate our system’s performance on the realworld KITTI-360 benchmark dataset. Our experimental results show that our system is capable of accurate, metric camera tracking and dense surface reconstruction within local windows. Our system operates within real-time processing rates and in challenging conditions. We direct the reader to the following video where the system can be seen in operation: https://***/Y-9q_wfqocs.

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shihong Wang Ruixun Liu Kaiyu Li Jiawei Jiang Xiangyong Cao Xi’an Jiaotong University China Sun Yat-Sen University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In Generalized Few-shot Segmentation (GFSS), a model is trained with a large set of base class samples and then adapted on limited samples of novel classes. This paper focuses on the relevance between base and novel classes, and improves GFSS in two aspects: 1) mining the similarity between base and novel classes to promote the learning of novel classes, and 2) mitigating the class imbalance issue caused by the volume difference between the support set and the training set. Specifically, we first propose a similarity transition matrix to guide the learning of novel classes with base class knowledge. Then, we leverage the Label-Distribution-Aware Margin (LDAM) loss and Trans-ductive Inference to the GFSS task to address the problem of class imbalance as well as overfitting the support set. In addition, by extending the probability transition matrix, the proposed method can mitigate the catastrophic forgetting of base classes when learning novel classes. With a simple training phase, our proposed method can be applied to any segmentation network trained on base classes. We validated our methods on the adapted version of OpenEarthMap. Compared to existing GFSS baselines, our method excels them all from 3% to 7% and ranks second in the OpenEarthMap Land Cover Mapping Few-Shot Challenge at the completion of this paper.

关键词： Training computer vision Adaptation models conferences Land surface pattern recognition

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