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All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers

All You Can Embed: Natural Language based Vehicle Retrieval ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Scribano, Carmelo Sapienza, Davide Franchini, Giorgia Verucchi, Micaela Bertogna, Marko Univ Modena & Reggio Emilia Modena Italy Univ Ferrara Ferrara Italy Univ Parma Parma Italy

ISBN: (纸本)9781665448994

Combining Natural Language with vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the problem of combining visual and textual information, applied to a smart-city use case. In this paper, we present All You Can Embed (AYCE), a modular solution to correlate single-vehicle tracking sequences with natural language. The main building blocks of the proposed architecture are (i) BERT to provide an embedding of the textual descriptions, (ii) a convolutional backbone along with a Transformer model to embed the visual information. For the training of the retrieval model, a variation of the Triplet Margin Loss is proposed to learn a distance measure between the visual and language embeddings. The code is publicly available at https://***/cscribano/AYCE_2021.

关键词： Training Visualization Target tracking Natural languages Urban areas computer architecture Loss measurement

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Structured Scene Memory for vision-Language Navigation

Structured Scene Memory for Vision-Language Navigation

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Hanqing Wang, Wenguan Liang, Wei Xiong, Caiming Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Salesforce Res San Francisco CA USA Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.

关键词： Visualization Three-dimensional displays Recurrent neural networks Navigation Decision making Layout Memory architecture

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Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization

Multi-Session SLAM with Differentiable Wide-Baseline Pose Op...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Lahav Lipson Jia Deng Princeton University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a new system for Multi-Session SLAM, which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. The backbone is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system can connect disjoint sequences, perform visualodometry, and global optimization. Compared to existing approaches, our design is accurate and robust to catas-trophic failures. Code is available at h t tps://github. com/princeton-v1/MultiSlam_DiffPose

关键词： Simultaneous localization and mapping Codes Accuracy Tracking Cameras pattern recognition Optimization

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Positional Encoding as Spatial Inductive Bias in GANs

Positional Encoding as Spatial Inductive Bias in GANs

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xu, Rui Wang, Xintao Chen, Kai Zhou, Bolei Loy, Chen Change Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation.

关键词： Training computer vision Image coding Image synthesis computer architecture Generators Encoding

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Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Yang, Ming-Hsuan Shao, Ling Incept Inst AI Abu Dhabi U Arab Emirates Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Monash Univ Clayton Vic Australia Univ Calif Merced Merced CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a twofaceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://***/swz30/MPRNet.

关键词： Runtime Computational modeling Noise reduction computer architecture Performance gain Image restoration pattern recognition

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Combining Magnification and Measurement for Non-Contact Cardiac Monitoring

Combining Magnification and Measurement for Non-Contact Card...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Nowara, Ewa M. McDuff, Daniel Veeraraghavan, Ashok Rice Univ Houston TX 77251 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Deep learning approaches currently achieve the state-of-the-art results on camera-based vital signs measurement. One of the main challenges with using neural models for these applications is the lack of sufficiently large and diverse datasets. Limited data increases the chances of overfitting models to the available data which in turn can harm generalization. In this paper, we show that the generalizability of imaging photoplethysmography models can be improved by augmenting the training set with "magnified" videos. These augmentations are specifically designed to reveal useful features for recovering the photoplethysmogram. We show that using augmentations of this form is more effective at improving model robustness than other commonly used data augmentation approaches. We show better within-dataset and especially cross-dataset performance with our proposed data augmentation approach on three publicly available datasets.

关键词： Training Deep learning Photoplethysmography Data models Skin Robustness pattern recognition

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Deep Dual Consecutive Network for Human Pose Estimation

Deep Dual Consecutive Network for Human Pose Estimation

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Zhenguang Chen, Haoming Feng, Runyang Wu, Shuang Ji, Shouling Yang, Bailin Wang, Xun Zhejiang Gongshang Univ Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images, their performances come short when we apply these models to video sequences. Prevalent shortcomings include the failure to handle motion blur, video defocus, or pose occlusions, arising from the inability in capturing the temporal dependency among video frames. On the other hand, directly employing conventional recurrent neural networks incurs empirical difficulties in modeling spatial contexts, especially for dealing with pose occlusions. In this paper, we propose a novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection. Three modular components are designed in our framework. A Pose Temporal Merger encodes keypoint spatiotemporal context to generate effective searching scopes while a Pose Residual Fusion module computes weighted pose residuals in dual directions. These are then processed via our Pose Correction Network for efficient refining of pose estimations. Our method ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have released our code, hoping to inspire future research.

关键词： computer vision Recurrent neural networks Corporate acquisitions Pose estimation Video sequences Refining Detectors

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Yang, Xueting Deng, Junli Chen, Rongshan Cong, Ruixuan Ke, Wei Sheng, Hao Communication University of China School of Information and Communication Engineering Beijing100024 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation. © 2023 ieee.

关键词： Semantics

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Neural Visibility Field for Uncertainty-Driven Active Mapping

Neural Visibility Field for Uncertainty-Driven Active Mappin...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shangjie Xue Jesse Dill Pranay Mathur Frank Dellaert Panagiotis Tsiotra Danfei Xu Georgia Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radi-ance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region, resulting in increased uncertainty in the synthesized views. To address this, we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently, NVF nat-urally assigns higher uncertainty to unobserved regions, aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in un-certainty quantification but also in scene reconstruction for active mapping, outperforming existing methods. More de-tails can be found at https://***/view/nvf-cvpr24/.

关键词： Training Visualization computer vision Uncertainty Robot vision systems Pipelines Neural radiance field

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