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Patch-Craft Self-Supervised Training for Correlated Image Denoising

Patch-Craft Self-Supervised Training for Correlated Image De...

引用

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

作者： Vaksman, Gregory Elad, Michael Technion CS Dept Haifa Israel

ISBN: (纸本)9798350301298

Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise.

关键词： Low-level vision

来源：评论

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ScaleDet: A Scalable Multi-Dataset Object Detector

ScaleDet: A Scalable Multi-Dataset Object Detector

引用

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

作者： Chen, Yanbei Wang, Manchen Mittal, Abhay Xu, Zhenlin Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.

关键词： Efficient and scalable vision

来源：评论

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Self-Supervised Normalizing Flows for Image Anomaly Detection and Localization

Self-Supervised Normalizing Flows for Image Anomaly Detectio...

引用

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

作者： Chiu, Li-Ling Lai, Shang-Hong National Tsing Hua University Department of Computer Science Taiwan

ISBN: (纸本)9798350302493

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods treat anomaly detection as an unsupervised task because anomalous training data and labels are usually scarce or unavailable. Recently, image synthesis has been used to generate anomalous samples which deviate from normal sample distribution for model training. By using the synthesized anomalous training samples, we present a novel self-supervised normalizing flow-based density estimation model, which is trained by maximizing the likelihood of normal images and minimizing the likelihood of synthetic anomalous images. By adding constraints to abnormal samples in our loss function, our model training is focused on normal samples rather than synthetic samples. Moreover, we improve the transformation subnet of the affine coupling layers in our flow-based model by dynamic stacking convolution and self-attention blocks. We evaluate our method on MVTec-AD, BTAD, and DAGM datasets and achieve state-of-the-art performance compared to flow-based and self-supervised methods on both anomaly detection and localization tasks. © 2023 ieee.

关键词： Anomaly detection

来源：评论

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Adaptive Human Matting for Dynamic Videos

Adaptive Human Matting for Dynamic Videos

引用

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

作者： Lin, Chung-Ching Wang, Jiang Luo, Kun Lin, Kevin Li, Linjie Wang, Lijuan Liu, Zicheng Microsoft Redmond WA 98052 USA

ISBN: (纸本)9798350301298

The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://***/microsoft/AdaM.

关键词： vision applications and systems

来源：评论

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Fast Point Cloud Generation with Straight Flows

Fast Point Cloud Generation with Straight Flows

引用

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

作者： Wu, Lemeng Wang, Dilin Gong, Chengyue Liu, Xingchao Xiong, Yunyang Ranjan, Rakesh Krishnamoorthi, Raghuraman Chandra, Vikas Liu, Qiang Univ Texas Austin Austin TX 78712 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350301298

Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.

关键词： Efficient and scalable vision

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Motion Information Propagation for Neural Video Compression

Motion Information Propagation for Neural Video Compression

引用

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

作者： Qi, Linfeng Li, Jiahao Li, Bin Li, Houqiang Lu, Yan Univ Sci & Technol China Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In most existing neural video codecs, the information flow therein is uni-directional, where only motion coding provides motion vectors for frame coding. In this paper, we argue that, through information interactions, the synergy between motion coding and frame coding can be achieved. We effectively introduce bi-directional information interactions between motion coding and frame coding via our Motion Information Propagation. When generating the temporal contexts for frame coding, the high-dimension motion feature from the motion decoder serves as motion guidance to mitigate the alignment errors. Meanwhile, besides assisting frame coding at the current time step, the feature from context generation will be propagated as motion condition when coding the subsequent motion latent. Through the cycle of such interactions, feature propagation on motion coding is built, strengthening the capacity of exploiting long-range temporal correlation. In addition, we propose hybrid context generation to exploit the multi-scale context features and provide better motion condition. Experiments show that our method can achieve 12.9% bit rate saving over the previous SOTA neural video codec.

关键词： Low-level vision

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MobileOne: An Improved One millisecond Mobile Backbone

MobileOne: An Improved One millisecond Mobile Backbone

引用

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

作者： Vasu, Pavan Kumar Anasosalu Gabriel, James Zhu, Jeff Tuzel, Oncel Ranjan, Anurag Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350301298

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. Code and models are available at https://***/apple/ml-mobileone

关键词： Efficient and scalable vision

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Affordance Grounding from Demonstration Video to Target Image

Affordance Grounding from Demonstration Video to Target Imag...

引用

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

作者： Chen, Joya Gao, Difei Lin, Kevin Qinghong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore

ISBN: (纸本)9798350301298

Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. This video-to-image affordance grounding task is challenging due to (1) the need to predict fine-grained affordances, and (2) the limited training data, which inadequately covers video-image discrepancies and negatively impacts grounding. To tackle them, we propose Affordance Transformer (Afformer), which has a fine-grained transformer-based decoder that gradually refines affordance grounding. Moreover, we introduce Mask Affordance Hand (MaskAHand), a self-supervised pre-training technique for synthesizing video-image data and simulating context changes, enhancing affordance grounding across video-image discrepancies. Afformer with MaskAHand pre-training achieves state-of-the-art performance on multiple benchmarks, including a substantial 37% improvement on the OPRA dataset. Code is made available at https://***/showlab/afformer.

关键词： vision applications and systems

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Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

Efficient and Explicit Modelling of Image Hierarchies for Im...

引用

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

作者： Li, Yawei Fan, Yuchen Xiang, Xiaoyu Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Meta Real Labs Menlo Pk CA 33137 USA Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://***/ofsoundof/***.

关键词： Low-level vision

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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

引用

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

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 ieee.

关键词： Iterative methods

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