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Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation

Orientation-conditioned Facial Texture Mapping for Video-bas...

引用

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

作者： Cantrill, Sam Ahmedt-Aristizabal, David Petersson, Lars Suominen, Hanna Armin, Mohammad Ali Australian Natl Univ Canberra ACT Australia Commonwealth & Sci Ind Res Org Data61 Canberra ACT Australia Univ Turku Turku Finland

ISBN: (纸本)9798350365474

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this study, we leverage the 3D facial surface to construct a novel orientation-conditioned facial texture video representation which improves the motion robustness of existing video-based facial rPPG estimation methods. Our proposed method achieves a significant 18.2% performance improvement in cross-dataset testing on MMPD over our baseline using the PhysNet model trained on PURE, highlighting the efficacy and generalization benefits of our designed video representation. We demonstrate significant performance improvements of up to 29.6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion. Emphasizing the benefits the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation. We validate the efficacy of our design decisions and the impact of different video processing steps through an ablation study. Our findings illustrate the potential strengths of exploiting the 3D facial surface as a general strategy for addressing dynamic and unconstrained subject motion in videos. The code is available at https://***/orientation-uv-rppg/.

关键词： computer vision motion rppg

来源：评论

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

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

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi Jilin Univ Sch Artificial Intelligence Jilin Peoples R China Zhejiang Univ Hangzhou Peoples R China Wayne State Univ Dept Comp Sci Wayne NJ USA MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China

ISBN: (纸本)9798350301298

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

关键词： vision + graphics

来源：评论

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Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

Gated Multi-Resolution Transfer Network for Burst Restoratio...

引用

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

作者： Mehta, Nancy Dudhane, Akshay Murala, Subrahmanyam Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Indian Inst Technol Ropar CVPR Lab Rupnagar India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350301298

Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMT-Net consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validate our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. Our codes and models are available at https://***/nanmehta/GMTNet.

关键词： Low-level vision

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An In-depth Exploration of Person Re-identification and Gait recognition in Cloth-Changing Conditions

An In-depth Exploration of Person Re-identification and Gait...

引用

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

作者： Li, Weijia Hou, Saihui Zhang, Chunjie Cao, Chunshui Liu, Xu Huang, Yongzhen Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9798350301298

The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://***/BNU-IVC/CCPG.

关键词： Datasets and evaluation

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Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Event-based Video Frame Interpolation with Cross-Modal Asymm...

引用

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

作者： Kim, Taewoo Chae, Yujeong Jang, Hyun-Kurl Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350301298

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://***/intelpro/CBMNet

关键词： Low-level vision

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Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations

Mask-free OVIS: Open-Vocabulary Instance Segmentation withou...

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

作者： Vibashan, V. S. Yu, Ning Xing, Chen Qin, Can Gao, Mingfei Nieblest, Juan Carlos Patel, Vishal M. Xu, Ran Johns Hopkins Univ Baltimore MD 21218 USA Northeastern Univ Boston MA 02115 USA Salesforce Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to annotate novel (new) categories. To alleviate this problem, Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-language models to learn novel categories. In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs. This difference between strong and weak supervision leads to overfitting on base categories, resulting in poor generalization towards novel categories. In this work, we overcome this issue by learning both base and novel categories from pseudomask annotations generated by the vision-language model in a weakly supervised manner using our proposed Mask-free OVIS pipeline. Our method automatically generates pseudo-mask annotations by leveraging the localization ability of a pre-trained vision-language model for objects present in image-caption pairs. The generated pseudomask annotations are then used to supervise an instance segmentation model, freeing the entire pipeline from any labour-expensive instance-level annotations and overfitting. Our extensive experiments show that our method trained with just pseudo-masks significantly improves the mAP scores on the MS-COCO dataset and OpenImages dataset compared to the recent state-of-the-art methods trained with manual masks. Codes and models are provided in https://***/ovis-web/.

关键词： detection recognition: Categorization retrieval

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Internal Diverse Image Completion

Internal Diverse Image Completion

引用

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

作者： Alkobi, Noa Shaham, Tamar Rott Michaeli, Tomer Technion Haifa Israel MIT Technion Haifa Israel

ISBN: (纸本)9798350302493

Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.

关键词： computer vision

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

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

作者： Choi, Haram Lee, Jeongmin Yang, Jihoon Sogang Univ Dept Comp Sci & Engn Seoul South Korea LG Innotek Seoul South Korea

ISBN: (纸本)9798350301298

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

关键词： Low-level vision

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OCTET: Object-aware Counterfactual Explanations

OCTET: Object-aware Counterfactual Explanations

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

作者： Zemni, Mehdi Chen, Mickael Zablocki, Eloi Ben-Younes, Hedi Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798350301298

Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image that would also change the output of the model to be explained. Such explanations point end-users at the main factors that impact the decision of the model. However, previous methods struggle to explain decision models trained on images with many objects, e.g., urban scenes, which are more difficult to work with but also arguably more critical to explain. In this work, we propose to tackle this issue with an object-centric framework for counterfactual explanation generation. Our method, inspired by recent generative modeling works, encodes the query image into a latent space that is structured in a way to ease object-level manipulations. Doing so, it provides the end-user with control over which search directions (e.g., spatial displacement of objects, style modification, etc.) are to be explored during the counterfactual generation. We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e.g., to explain semantic segmentation models. To complete our analysis, we design and run a user study that measures the usefulness of counterfactual explanations in understanding a decision model. Code is available at https://***/valeoai/OCTET.

关键词： Explainable computer vision

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IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

IrrNet: Advancing Irrigation Mapping with Incremental Patch ...

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

作者： Hoque, Oishee Bintey Swarup, Samarth Adiga, Abhijin Nouwakpo, Sayjro Kossi Marathe, Madhav Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Virginia Biocomplex Inst Charlottesville VA 22903 USA ARS USDA Kimberly ID USA

ISBN: (纸本)9798350365474

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat's remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.

关键词： computer vision Irrigation Mapping Patch Scaling Remote Sensing Segmentation Transfer learning

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