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Hephaestus: A large scale multitask dataset towards InSAR understanding

Hephaestus: A large scale multitask dataset towards InSAR un...

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

作者： Bountos, Nikolaos Ioannis Papoutsis, Ioannis Michail, Dimitrios Karavias, Andreas Elias, Panagiotis Parcharidis, Isaak Natl Observ Athens Inst Astron Astrophys Space Applicat & Remote Sen Athens Greece Harokopio Univ Athens Dept Informat & Telemat Athens Greece Harokopio Univ Athens Dept Geog Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, and on the geotechnical properties of man-made structures. However, there are only a limited number of applications that exploit the abundance of InSAR data and deep learning methods to extract such knowledge. The main barrier has been the lack of a large curated and annotated InSAR dataset, which would be costly to create and would require an interdisciplinary team of experts experienced on InSAR data interpretation. In this work, we put the effort to create and make available the first of its kind, manually annotated dataset that consists of 19,919 individual Sentinel-1 interferograms acquired over 44 different volcanoes globally, which are split into 216,106 InSAR patches. The annotated dataset is designed to address different computer vision problems, including volcano state classification, semantic segmentation of ground deformation, detection and classification of atmospheric signals in InSAR imagery, interferogram captioning, text to InSAR generation, and InSAR image quality assessment.

关键词： Deep learning Image quality computer vision Image segmentation Semantics Volcanoes Task analysis

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Coupling vision and Proprioception for Navigation of Legged Robots

Coupling Vision and Proprioception for Navigation of Legged ...

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

作者： Fu, Zipeng Kumar, Ashish Agarwal, Ananye Qi, Haozhi Malik, Jitendra Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint highlevel planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://***

关键词： Legged locomotion Navigation Robot vision systems Robot sensing systems Generators Sensors Safety

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Inferring Affective Experience from the Big Picture Metaphor: A Two-dimensional Visual Breadth Model

Inferring Affective Experience from the Big Picture Metaphor...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Tong, Song Duan, Jingyi Liang, Xuefeng Kumada, Takatsune Peng, Kaiping Nakashima, Ryoichi Tsinghua University Department of Psychology Beijing China Xidian University School of Artificial Intelligence Shaanxi China Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798350302493

This study explores the psychological significance of the commonly used visual metaphor 'seeing the big picture' and examines whether and how it leads to positive experiences in real-life situations. To elucidate this phenomenon, a two-dimensional model of visual breadth is proposed, then respectively operationalized by two computer vision approaches. Our approaches are evaluated on a collected data set with 29,216 photos. The results revealed that physical and contextual breadth are two essential visual structures that compose a 'big picture'. Furthermore, these two visual breadths interactively shape people's affective experiences. This study provides insight into the psychological implications of the 'big picture' metaphor and sheds light on its practical potential for computer vision approaches and affective computing in the wild. © 2023 ieee.

关键词： computer vision

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DRT: A Lightweight Single Image Deraining Recursive Transformer

DRT: A Lightweight Single Image Deraining Recursive Transfor...

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

作者： Liang, Yuanchu Anwar, Saeed Liu, Yang Australian Natl Univ Canberra Australia CSIRO Data61 Sydney Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task;nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only similar to 1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://***/YC-Liang/DRT.

关键词： Training Deep learning computer vision Rain Computational modeling computer architecture Transformers

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GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting

GLaMa: Joint Spatial and Frequency Loss for General Image In...

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

作者： Lu, Zeyu Jiang, Junjun Huang, Junqin Wu, Gang Liu, Xianming Harbin Inst Technol Harbin Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional neural networks (CNNs), image inpainting task has made great breakthroughs. However, most of the work consider insufficient types of mask, and their performance will drop dramatically when encountering unseen masks. To combat these challenges, we propose a simple yet general method to solve this problem based on the LaMa image inpainting framework [35], dubbed GLaMa. Our proposed GLaMa can better capture different types of missing information by using more types of masks. By incorporating more degraded images in the training phase, we can expect to enhance the robustness of the model with respect to various masks. In order to yield more reasonable results, we further introduce a frequency-based loss in addition to the traditional spatial reconstruction loss and adversarial loss. In particular, we introduce an effective reconstruction loss both in the spatial and frequency domain to reduce the chessboard effect and ripples in the reconstructed image. Extensive experiments demonstrate that our method can boost the performance over the original LaMa method for each type of mask on FFHQ [18], ImageNet [7], Places2 [42] and WikiArt [28] dataset. The proposed GLaMa was ranked first in terms of PSNR, LPIPS [39] and SSIM [34] in the NTIRE 2022 Image Inpainting Challenge Track 1 Unsupervised [27].

关键词： Training computer vision Frequency-domain analysis conferences computer architecture Robustness pattern recognition

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ActAR: Actor-Driven Pose Embeddings for Video Action recognition

ActAR: Actor-Driven Pose Embeddings for Video Action Recogni...

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

作者： Lamghari, Soufiane Bilodeau, Guillaume-Alexandre Saunier, Nicolas Polytech Montreal Dept Comp & Software Engn LITIV Lab Montreal PQ Canada Polytech Montreal Dept Civil Geol & Min Engn Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human action recognition (HAR) in videos is one of the core tasks of video understanding. Based on video sequences, the goal is to recognize actions performed by humans. While HAR has received much attention in the visible spectrum, action recognition in infrared videos is little studied. Accurate recognition of human actions in the infrared domain is a highly challenging task because of the redundant and indistinguishable texture features present in the sequence. Furthermore, in some cases, challenges arise from the irrelevant information induced by the presence of multiple active persons not contributing to the actual action of interest. Therefore, most existing methods consider a standard paradigm that does not take into account these challenges, which is in some part due to the ambiguous definition of the recognition task in some cases. In this paper, we propose a new method that simultaneously learns to recognize efficiently human actions in the infrared spectrum, while automatically identifying the key-actors performing the action without using any prior knowledge or explicit annotations. Our method is composed of three stages. In the first stage, optical flow-based key-actor identification is performed. Then for each key-actor, we estimate key-poses that will guide the frame selection process. A scale-invariant encoding process along with embedded pose filtering are performed in order to enhance the quality of action representations. Experimental results on InfAR dataset show that our proposed model achieves promising recognition performance and learns useful action representations.

关键词： Training Optical filters Filtering Annotations Computational modeling Video sequences pattern recognition

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Shared Interest...Sometimes: Understanding the Alignment between Human Perception, vision Architectures, and Saliency Map Techniques

Shared Interest...Sometimes: Understanding the Alignment bet...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Morrison, Katelyn Mehra, Ankita Perer, Adam

ISBN: (纸本)9798350302493

Empirical studies have shown that attention-based architectures outperform traditional convolutional neural networks (CNN) in terms of accuracy and robustness. As a result, attention-based architectures are increasingly used in high-stakes domains such as radiology and wildlife conservation to aid in decision-making. However, understanding how attention-based architectures compare to CNNs regarding alignment with human perception is still under-explored. Previous studies exploring how vision architectures align with human perception evaluate a single architecture with multiple explainability techniques or multiple architectures with a single explainability technique. Through an empirical analysis, we investigate how two attention-based architectures and two CNNs for two saliency map techniques align with the ground truth for human perception on 100 images from an interpretability benchmark dataset. Using the Shared Interest metrics, we found that CNNs align more with human perception when using the XRAI saliency map technique. However, we found the opposite for Grad-CAM. We discuss the implications of our analysis for human-centered explainable AI and introduce directions for future work. © 2023 ieee.

关键词： Decision making

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Identity Preserving Loss for Learned Image Compression

Identity Preserving Loss for Learned Image Compression

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

作者： Xiao, Jiuhong Aggarwal, Lavisha Banerjee, Prithviraj Aggarwal, Manoj Medioni, Gerard NYU 550 1St Ave New York NY 10012 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning model inference on embedded devices is challenging due to the limited availability of computation resources. A popular alternative is to perform model inference on the cloud, which requires transmitting images from the embedded device to the cloud. Image compression techniques are commonly employed in such cloud-based architectures to reduce transmission latency over low bandwidth networks. This work proposes an end-to-end image compression framework that learns domain-specific features to achieve higher compression ratios than standard HEVC/JPEG compression techniques while maintaining accuracy on downstream tasks (e.g., recognition). Our framework does not require fine-tuning of the downstream task, which allows us to drop-in any off-the-shelf downstream task model without retraining. We choose faces as an application domain due to the ready availability of datasets and off-the-shelf recognition models as representative downstream tasks. We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are similar to 38% and similar to 42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy. The superior compression ratio is achieved as the model learns to retain the domain-specific features (e.g., facial features) while sacrificing details in the background. Furthermore, images reconstructed by our proposed compression model are robust to changes in downstream model architectures. We show at-par recognition performance on the LFW dataset with an unseen recognition model while retaining a lower BPP value of similar to 38% of CRF-23 HEVC compression.

关键词： Image coding Image recognition Face recognition Computational modeling Intellectual property computer architecture Robustness

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Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

Goal-driven Self-Attentive Recurrent Networks for Trajectory...

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

作者： Chiara, Luigi Filippo Coscia, Pasquale Das, Sourav Calderara, Simone Cucchiara, Rita Ballan, Lamberto Univ Padua Padua Italy Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multimodality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.

关键词： Measurement Semantics Stochastic processes computer architecture Trajectory Data mining Task analysis

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From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

From Less to More: Spectral Splitting and Aggregation Networ...

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

作者： Jiang, Junjun Wang, Chenyang Liu, Xianming Jiang, Kui Ma, Jiayi Harbin Inst Technol Harbin Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofing attacks. However, the dense spectral bands of hyperspectral face images come at the cost of limited amount of photons reached a narrow spectral window on average, which greatly reduces the spatial resolution of hyperspectral face images. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution (HFSR), especially when the training samples are very limited. Benefiting from the amount of spectral bands, in which each band can be seen as an image, we present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples. In the shallow layers, we split the hyperspectral image into different spectral groups. Then, we gradually aggregate the neighbor bands at deeper layers to exploit spectral correlations. By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (from less to more) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum. To cope with the challenge of small training sample size (S3) problem, we propose to expand the training samples by a self-representation model and symmetry-induced augmentation. Experiments show that SSANet can well model the joint correlations of spatial and spectral information. By expanding the training samples, SSANet can effectively alleviate the S3 problem.

关键词： Training computer vision Correlation Face recognition Superresolution Task analysis Spatial resolution

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