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FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian...

引用

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

作者： Byun, Jaeseok Cha, Sungmin Moon, Taesup Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781665445092

We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which no additional information about clean images or noise level parameters is available. Particularly, when only "single" noisy images are available for training a denoiser, the denoising performance of existing methods was not satisfactory. Recently, the blind pixelwise affine image denoiser (BP-AIDE) was proposed and significantly improved the performance in the above setting, to the extent that it is competitive with denoisers which utilized additional information. However, BP-AIDE seriously suffered from slow inference time due to the inefficiency of noise level estimation procedure and that of the blind-spot network (BSN) architecture it used. To that end, we propose Fast Blind Image Denoiser (FBI-Denoiser) for Poisson-Gaussian noise, which consists of two neural network models;1) PGE-Net that estimates Poisson-Gaussian noise parameters 2000 times faster than the conventional methods and 2) FBI-Net that realizes a much more efficient BSN for pixelwise affine denoiser in terms of the number of parameters and inference speed. Consequently, we show that our FBI-Denoiser blindly trained solely based on single noisy images can achieve the state-of-the-art performance on several real-world noisy image benchmark datasets with much faster inference time ( x 10), compared to BP-AIDE.

关键词： Training computer vision Noise reduction Neural networks Estimation computer architecture Benchmark testing

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Dancing under the stars: video denoising in starlight

Dancing under the stars: video denoising in starlight

引用

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

作者： Monakhova, Kristina Richter, Stephan R. Waller, Laura Koltun, Vladlen Uc Berkeley United States Intel Labs

Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we dem... 详细信息

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

关键词： Semiconductor device modeling Noise reduction Lighting Stars Cameras Quality assessment pattern recognition

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AdaMixer: A Fast-Converging Query-Based Object Detector

AdaMixer: A Fast-Converging Query-Based Object Detector

引用

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

作者： Gao, Ziteng Wang, Limin Han, Bing Guo, Sheng Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Ant Grp MYbank Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding image features with a set of learnable queries. However, this paradigm still suffers from slow convergence, limited performance, and design complexity of extra networks between backbone and decoder. In this paper, we find that the key to these issues is the adaptability of decoders for casting queries to varying objects. Accordingly, we propose a fast-converging query-based detector, named AdaMixer, by improving the adaptability of query-based decoding processes in two aspects. First, each query adaptively samples features over space and scales based on estimated offsets, which allows AdaMixer to efficiently attend to the coherent regions of objects. Then, we dynamically decode these sampled features with an adaptive MLP-Mixer under the guidance of each query. Thanks to these two critical designs, AdaMixer enjoys architectural simplicity without requiring dense attentional encoders or explicit pyramid networks. On the challenging MS COCO benchmark, AdaMixer with ResNet-50 as the backbone, with 12 training epochs, reaches up to 45.0 AP on the validation set along with 27.9 APs in detecting small objects. With the longer training scheme, AdaMixer with ResNeXt-101-DCN and Swin-S reaches 49.5 and 51.3 AP. Our work sheds light on a simple, accurate, and fast converging architecture for query-based object detectors. The code is made available athttps://***/MCG-JU/AdaMixer.

关键词： Training computer vision Navigation Detectors computer architecture Feature extraction Decoding

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All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation 30

All You Need is Beyond a Good Init: Exploring Better Solutio...

引用

30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xie, Di Xiong, Jiang Pu, Shiliang Hikvis Res Inst Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538604571

Deep neural network is difficult to train and this predicament becomes worse as the depth increases. The essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing or exploding phenomenon. We show that a variant of regularizer which utilizes orthonormality among different filter banks can alleviate this problem. Moreover, we design a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers. Equipped with these two ingredients, we propose several novel optimization solutions that can be utilized for training a specific-structured (repetitively triple modules of Conv-BNReLU) extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/identity mappings from scratch. Experiments show that our proposed solutions can achieve distinct improvements for a 44-layer and a 110-layer plain networks on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts. Besides, we propose new principles for designing network structure from the insights evoked by orthonormality. Combined with residual structure, we achieve comparative performance on the ImageNet dataset.

关键词： Modulation Neural networks Training Jacobian matrices Convolution computer vision Network architecture

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Gated Fields: Learning Scene Reconstruction from Gated Videos

Gated Fields: Learning Scene Reconstruction from Gated Video...

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

作者： Ramazzina, Andrea Walz, Stefanie Dahal, Pragyan Bijelic, Mario Heide, Felix Mercedes Benz Stuttgart Germany Saarland Univ Saarbrucken Germany Politecn Milan Milan Italy Torc Robot Blacksburg VA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes with scanning LiDAR sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps, we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end, we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our code and datasets are available here(1).

关键词： 3D vision Gated Camera Neural Radiance Fields Neural Rendering

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

引用

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

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

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Image Dehazing Transformer with Transmission-Aware 3D Position Embedding

Image Dehazing Transformer with Transmission-Aware 3D Positi...

引用

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

作者： Guo, Chunle Yan, Qixin Anwar, Saeed Cong, Runmin Ren, Wenqi Li, Chongyi Nankai Univ TMCC CS Tianjin Peoples R China Tianjin Univ Tianjin Peoples R China Australian Natl Univ Canberra ACT Australia Beijing Jiaotong Univ Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite single image dehazing has been made promising progress with Convolutional Neural Networks (CNNs), the inherent equivariance and locality of convolution still bottleneck dehazing performance. Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: I) it tends to result in ambiguous and coarse details that are undesired for image reconstruction;2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance. The key insight of this study is to investigate how to combine CNN and Transformer for image dehazing. To solve the feature inconsistency issue between Transformer and CNN, we propose to modulate CNN features via learning modulation matrices (i.e., coefficient matrix and bias matrix) conditioned on Transformer features instead of simple feature addition or concatenation. The feature modulation naturally inherits the global context modeling capability of Transformer and the local representation capability of CNN. We bring a haze density-related prior into Transformer via a novel transmission-aware 3D position embedding module, which not only provides the relative position but also suggests the haze density of different spatial regions. Extensive experiments demonstrate that our method, DeHamer, attains state-of-the-art performance on several image dehazing benchmarks.

关键词： computer vision Three-dimensional displays Convolution Modulation Benchmark testing Transformers pattern recognition

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CRAVES: Controlling Robotic Arm with a vision-based Economic System 32

CRAVES: Controlling Robotic Arm with a Vision-based Economic...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zuo, Yiming Qiu, Weichao Xie, Lingxi Zhong, Fangwei Wang, Yizhou Yuille, Alan L. Tsinghua Univ Beijing Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA Peking Univ Beijing Peoples R China Huawei Inc Noahs Ark Lab Shenzhen Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China DeepWise AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which filly lever-ages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.

关键词： 3D from Single Image Robotics + Driving vision Applications and Systems

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EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Vis...

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

作者： Wu, Yanmin Cheng, Xinhua Zhang, Renrui Cheng, Zesen Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, Scan-Refer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://***/yanmin-wu/EDA.

关键词： language reasoning vision

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Scaling Up vision-Language Pre-training for Image Captioning

Scaling Up Vision-Language Pre-training for Image Captioning

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

作者： Hu, Xiaowei Gan, Zhe Wang, Jianfeng Yang, Zhengyuan Liu, Zicheng Lu, Yumao Wang, Lijuan Microsoft Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON(sic), a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art VinVL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million image-text pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M(1)). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail visual concepts when used in a zero-shot manner.

关键词： Training Visualization Computational modeling Training data Benchmark testing Transformers Feature extraction

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