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NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

NeRV: Neural Reflectance and Visibility Fields for Relightin...

引用

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

作者： Srinivasan, Pratul P. Deng, Boyang Zhang, Xiuming Tancik, Matthew Mildenhall, Ben Barron, Jonathan T. Google Res Mountain View CA 94043 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model's ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work.

关键词： Training Reflectivity computer vision Three-dimensional displays Lighting Predictive models Rendering (computer graphics)

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

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

作者： Han Zhou Wei Dong Yangyi Liu Jun Chen Department of Electrical and Computer Engineering Mcmaster University Hamilton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC.

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

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

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Semi-supervised Semantic Segmentation with Directional Conte...

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

作者： Lai, Xin Tian, Zhuotao Jiang, Li Liu, Shu Zhao, Hengshuang Wang, Liwei Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this paper, we focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. Nevertheless, due to the limited annotations, models may overly rely on the contexts available in the training data, which causes poor generalization to the scenes unseen before. A preferred high-level representation should capture the contextual information while not losing self-awareness. Therefore, we propose to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments. Moreover, we present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner, only requiring the feature with lower quality to be aligned towards its counterpart. In addition, to avoid the false-negative samples and filter the uncertain positive samples, we put forward two sampling strategies. Extensive experiments show that our simple yet effective method surpasses current state-of-the-art methods by a large margin and also generalizes well with extra image-level annotations.

关键词： Image segmentation computer vision Annotations Semantics Training data Data models pattern recognition

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FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

FESTA: Flow Estimation via Spatial-Temporal Attention for Sc...

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

作者： Wang, Haiyan Pang, Jiahao Lodhi, Muhammad A. Tian, Yingli Tian, Dong InterDigital Wilmington DE 19809 USA CUNY City Coll New York NY 10031 USA

ISBN: (纸本)9781665445092

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction-an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA(2)) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA(2)) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.

关键词： computer vision Three-dimensional displays Image recognition Image coding Navigation Computational modeling Estimation

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Medical Image Classification Using Generalized Zero Shot Learning 18

Medical Image Classification Using Generalized Zero Shot Lea...

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18th ieee/cvf International conference on computer vision (ICCV)

作者： Mahapatra, Dwarikanath Bozorgtabar, Behzad Ge, Zongyuan Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Ecole Polytech Fed Lausanne LTS5 Lausanne Switzerland CIBM Lausanne Switzerland Monash Univ Clayton Vic Australia Airdoc Res Melbourne Melbourne Vic Australia

ISBN: (纸本)9781665401913

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of different disease classes;and 2) training a feature generator. Our approach does not require class attribute vectors which are available for natural images but not for medical images. SSL ensures that the anchor vectors are representative of each class. SSL is also used to generate synthetic features of unseen classes. Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images. Our method is adaptable enough to accommodate class attribute vectors when they are available for natural images.

关键词： Training computer vision Image recognition conferences Supervised learning Semantics Generators

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EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

EyePAD++: A Distillation-based approach for joint Eye Authen...

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2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Dhar, Prithviraj Kumar, Amit Kaplan, Kirsten Gupta, Khushi Ranjan, Rakesh Chellappa, Rama Johns Hopkins University United States Meta Reality Labs

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the 'versatility' of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality. © 2022 ieee.

关键词： Training Performance evaluation Image quality Iris Protocols Machine vision Image edge detection

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Scene Text Retrieval via Joint Text Detection and Similarity Learning

Scene Text Retrieval via Joint Text Detection and Similarity...

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

作者： Wang, Hao Bai, Xiang Yang, Mingkun Zhu, Shenggao Wang, Jing Liu, Wenyu Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huawei Cloud & AI Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by directly learning a cross-modal similarity between a query text and each text instance from natural images. Specifically, we establish an end-to-end trainable network, jointly optimizing the procedures of scene text detection and cross-modal similarity learning. In this way, scene text retrieval can be simply performed by ranking the detected text instances with the learned similarity. Experiments on three benchmark datasets demonstrate our method consistently outperforms the state-of-the-art scene text spotting/retrieval approaches. In particular, the proposed framework of joint detection and similarity learning achieves significantly better performance than separated methods. Code is available at: https://***/lanfeng4659/STR-TDSL.

关键词： computer vision Codes Text recognition Benchmark testing Task analysis

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BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primit...

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

作者： Sainan Liu Shan Lin Jingpei Lu Alexey Supikov Michael Yip Intel Labs UCSD

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated sampling and hash encoding to expedite automatic pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate 10 to 20 × speed improvement compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://***/IntelLabs/baa-ngp.

关键词： Training Graphics Three-dimensional displays Robot vision systems Video sequences Streaming media Cameras

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AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

AdCo: Adversarial Contrast for Efficient Learning of Unsuper...

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

作者： Hu, Qianjiang Wang, Xiao Hu, Wei Qi, Guo-Jun Peking Univ Beijing Peoples R China Purdue Univ W Lafayette IN 47907 USA Lab MAchine Percept & LEarning MAPLE Beijing Peoples R China

ISBN: (纸本)9781665445092

Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over minibatches while only a small portion of them are updated in an iteration, or only use the other examples from the current minibatch as negatives. They could not closely track the change of the learned representation over iterations by updating the entire queue as a whole, or discard the useful information from the past minibatches. Alternatively, we present to directly learn a set of negative adversaries playing against the self-trained representation. Two players, the representation network and negative adversaries, are alternately updated to obtain the most challenging negative examples against which the representation of positive queries will be trained to discriminate. We further show that the negative adversaries are updated towards a weighted combination of positive queries by maximizing the adversarial contrastive loss, thereby allowing them to closely track the change of representations over time. Experiment results demonstrate the proposed Adversarial Contrastive (AdCo) model not only achieves superior performances (a top-1 accuracy of 73.2% over 200 epochs and 75.7% over 800 epochs with linear evaluation on ImageNet), but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs.

关键词： Learning systems computer vision Codes Graphics processing units pattern recognition Task analysis

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