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ElasticFace: Elastic Margin Loss for Deep Face recognition

ElasticFace: Elastic Margin Loss for Deep Face Recognition

引用

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

作者： Boutros, Fadi Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD D-64283 Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci D-64289 Germany Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs will be publicly released (1).

关键词： Training computer vision Codes Face recognition conferences Buildings Training data

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NeRF-Editing: Geometry Editing of Neural Radiance Fields

NeRF-Editing: Geometry Editing of Neural Radiance Fields

引用

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

作者： Yuan, Yu-Jie Sun, Yang-Tian Lai, Yu-Kun Ma, Yuewen Jia, Rongfei Gao, Lin Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users.

关键词： Geometry Deep learning computer vision Shape Image color analysis Neural networks Rendering (computer graphics)

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OpenSentinelMap: A Large-Scale Land Use Dataset using OpenStreetMap and Sentinel-2 Imagery

OpenSentinelMap: A Large-Scale Land Use Dataset using OpenSt...

引用

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

作者： Johnson, Noah Treible, Wayne Crispell, Daniel Vis Syst Inc Riverside RI 02915 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote sensing data is plentiful, but downloading, organizing, and transforming large amounts of data into a format readily usable by modern machine learning methods is a challenging and labor-intensive task. We present the OpenSentinelMap dataset, which consists of 137,045 unique 3.7 km(2) spatial cells, each containing multiple multispectral Sentinel-2 images captured over a 4 year time period and a set of corresponding per-pixel semantic labels derived from OpenStreetMap data. The labels are not necessarily mutually exclusive, and contain information about roads, buildings, water, and 12 land-use categories. The spatial cells are selected randomly on a global scale over areas of human activity, without regard to OpenStreetMap data availability or quality, making the dataset ideal for both supervised, semi-supervised, and unsupervised experimentation. To demonstrate the effectiveness of the dataset, we a) train an off-the-shelf convolutional neural network with minimal modification to predict land-use and building and road location from multispectral Sentinel-2 imagery and b) show that the learned embeddings are useful for downstream fine-grained classification tasks without any fine-tuning. The dataset is publicly available at https://***/open-sentinel-map/.

关键词： Training Annotations Roads Semantics Buildings pattern recognition Task analysis

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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Pseudo-label Generation for Agricultural Robotics Applications

Pseudo-label Generation for Agricultural Robotics Applicatio...

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

作者： Ciarfuglia, Thomas A. Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza Univ Rome Dept Comp Sci Management & Automat Engn DIAG Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available. In this work, we propose a semi-supervised solution to reduce the data needed to get state-of-the-art detection and segmentation of fruits in orchards. We present the case of table grape vineyards in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the concrete scenario where the source labelled data is wine grape, while the target data is table grape, with considerable covariate shift. Starting from a simple video input, our method generates first bounding box labels, leveraging the structure from motion information, then segmentation masks, using the same weakly generated bounding box labels and a refining step based on Grabcut. This system is able to produce labels that considerably reduce the covariate shift from source to target data and that requires very limited data acquisition effort. Comparisons with State-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images, with very simple labelling.

关键词： Structure from motion Motion segmentation Pipelines Refining Spraying Lighting pattern recognition

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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

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

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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Towards Real-world Shadow Removal with a Shadow Simulation Method and a Two-stage Framework

Towards Real-world Shadow Removal with a Shadow Simulation M...

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

作者： Gao, Jianhao Zheng, Quanlong Guo, Yandong Wuhan Univ Wuhan Peoples R China OPPO Res Inst Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://***/jianhaogao/Shadow-Removal-with-Two-stage-Framework.

关键词： Training computer vision Image color analysis conferences Computational modeling Gray-scale Robustness

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NICO++: Towards Better Benchmarking for Domain Generalization

NICO++: Towards Better Benchmarking for Domain Generalizatio...

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

作者： Zhang, Xingxuan He, Yue Xu, Renzhe Yu, Han Shen, Zheyan Cui, Peng Tsinghua University Department of Computer Science China

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for do-main generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DC algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, NlCO++ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection. The data and code for the benchmark based on NICO++ are available at https://***/xxgege/NICO-plus. © 2023 ieee.

关键词： Deep neural networks

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Embedding Arithmetic of Multimodal Queries for Image Retrieval

Embedding Arithmetic of Multimodal Queries for Image Retriev...

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

作者： Couairon, Guillaume Douze, Matthijs Cord, Matthieu Schwenk, Holger Meta AI Menlo Pk CA 94025 USA Sorbonne Univ Paris France Valeo St Ouen Sur Seine France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).

关键词： computer vision conferences Semantics Image retrieval Lasers Transforms Image representation

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VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

VFHQ: A High-Quality Dataset and Benchmark for Video Face Su...

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

作者： Xie, Liangbin Wang, Xintao Zhang, Honglun Dong, Chao Shan, Ying Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings.

关键词： Training Visualization Privacy Face recognition Superresolution Pipelines Benchmark testing

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