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共 22908 条记录，以下是4431-4440 订阅

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MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

MetaAlign: Coordinating Domain Alignment and Classification ...

引用

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

作者： Wei, Guoqiang Lan, Cuiling Zeng, Wenjun Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics. However, the optimization objective of such domain alignment is generally not coordinated with that of the object classification task itself such that their descent directions for optimization may be inconsistent. This will reduce the effectiveness of domain alignment in improving the performance of UDA. In this paper, we aim to study and alleviate the optimization inconsistency problem between the domain alignment and classification tasks. We address this by proposing an effective meta-optimization based strategy dubbed MetaAlign, where we treat the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme. MetaAlign encourages both tasks to be optimized in a coordinated way, which maximizes the inner product of the gradients of the two tasks during training. Experimental results demonstrate the effectiveness of our proposed method on top of various alignment-based baseline approaches, for tasks of object classification and object detection. MetaAlign helps achieve the state-of-the-art performance.

关键词： Training computer vision Object detection Adversarial machine learning pattern recognition Task analysis Optimization

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POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

POSEFusion: Pose-guided Selective Fusion for Single-view Hum...

引用

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

作者： Li, Zhe Yu, Tao Zheng, Zerong Guo, Kaiwen Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Google Zurich Switzerland

ISBN: (纸本)9781665445092

We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Cameras pattern recognition Dynamic programming

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Unsupervised Visual Attention and Invariance for Reinforcement Learning

Unsupervised Visual Attention and Invariance for Reinforceme...

引用

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

作者： Wang, Xudong Lian, Long Yu, Stella X. Univ Calif Berkeley ICSI Berkeley CA 94720 USA

ISBN: (纸本)9781665445092

vision-based reinforcement learning (RL) is successful, but how to generalize it to unknown test environments remains challenging. Existing methods focus on training an RL policy that is universal to changing visual domains, whereas we focus on extracting visual foreground that is universal, feeding clean invariant vision to the RL policy learner. Our method is completely unsupervised, without manual annotations or access to environment internals. Given videos of actions in a training environment, we learn how to extract foregrounds with unsupervised keypoint detection, followed by unsupervised visual attention to automatically generate a foreground mask per video frame. We can then introduce artificial distractors and train a model to reconstruct the clean foreground mask from noisy observations. Only this learned model is needed during test to provide distraction-free visual input to the RL policy learner. Our Visual Attention and Invariance (VAI) method significantly outperforms the state-of-the-art on visual domain generalization, gaining 15 similar to 49% (61 similar to 229%) more cumulative rewards per episode on DeepMind Control (our Drawer-World Manipulation) benchmarks. Our results demonstrate that it is not only possible to learn domain-invariant vision without any supervision, but freeing RL from visual distractions also makes the policy more focused and thus far better.

关键词： Training Visualization computer vision Annotations Reinforcement learning Manuals Benchmark testing

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AutoInt: Automatic Integration for Fast Neural Volume Rendering

AutoInt: Automatic Integration for Fast Neural Volume Render...

引用

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

作者： Lindell, David B. Martel, Julien N. P. Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10x with a tradeoff of reduced image quality.

关键词： Training Image quality computer vision Monte Carlo methods Scientific computing Neural networks Memory management

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DualAST: Dual Style-Learning Networks for Artistic Style Transfer

DualAST: Dual Style-Learning Networks for Artistic Style Tra...

引用

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

作者： Chen, Haibo Zhao, Lei Wang, Zhizhong Zhang, Huiming Zuo, Zhiwen Li, Ailin Xing, Wei Lu, Dongming Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method.

关键词： Visualization computer vision Image color analysis Controllability pattern recognition Task analysis

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Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

Style-Aware Normalized Loss for Improving Arbitrary Style Tr...

引用

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

作者： Cheng, Jiaxin Jaiswal, Ayush Wu, Yue Natarajan, Pradeep Natarajan, Prem USC Informat Sci Inst Arlington VA 22203 USA Amazon Alexa Nat Understanding Arlington VA USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). Although appealing results have been widely reported in literature, our empirical studies on four well-known AST approaches (GoogleMagenta [14], AdaIN [19], LinearTransfer [29], and SANet [37]) show that more than 50% of the time, AST stylized images are not acceptable to human users, typically due to under- or over-stylization. We systematically study the cause of this imbalanced style transferability (IST) and propose a simple yet effective solution to mitigate this issue. Our studies show that the IST issue is related to the conventional AST style loss, and reveal that the root cause is the equal weightage of training samples irrespective of the properties of their corresponding style images, which biases the model towards certain styles. Through investigation of the theoretical bounds of the AST style loss, we propose a new loss that largely overcomes IST. Theoretical analysis and experimental results validate the effectiveness of our loss, with over 80% relative improvement in style deception rate and 98% relatively higher preference in human evaluation.

关键词： Training computer vision pattern recognition

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Stochastic Image-to-Video Synthesis using cINNs

Stochastic Image-to-Video Synthesis using cINNs

引用

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

作者： Dorkenwald, Michael Milbich, Timo Blattmann, Andreas Rombach, Robin Derpanis, Konstantinos G. Ommer, Bjorn Heidelberg Univ IWR HCI Heidelberg Germany Ryerson Univ Dept Comp Sci Toronto ON Canada Vector Inst AI Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://***/3dg90fV.

关键词： computer vision Neural networks Stochastic processes Process control Predictive models Probabilistic logic pattern recognition

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Segment Anything in Medical Images with nnUNet 1

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Practical Wide-Angle Portraits Correction with Deep Structured Models

Practical Wide-Angle Portraits Correction with Deep Structur...

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

作者： Tan, Jing Zhao, Shan Xiong, Pengfei Liu, Jiangyu Fan, Haoqiang Liu, Shuaicheng Megvii Res Chengdu Peoples R China Tencent Chengdu Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Wide-angle portraits often enjoy expanded views. However, they contain perspective distortions, especially noticeable when capturing group portrait photos, where the background is skewed and faces are stretched. This paper introduces the first deep learning based approach to remove such artifacts from freely-shot photos. Specifically, given a wide-angle portrait as input, we build a cascaded network consisting of a LineNet, a ShapeNet, and a transition module (TM), which corrects perspective distortions on the background, adapts to the stereographic projection on facial regions, and achieves smooth transitions between these two projections, accordingly. To train our network, we build the first perspective portrait dataset with a large diversity in identities, scenes and camera modules. For the quantitative evaluation, we introduce two novel metrics, line consistency and face congruence. Compared to the previous state-of-the-art approach, our method does not require camera distortion parameters. We demonstrate that our approach significantly outperforms the previous state-of-the-art approach both qualitatively and quantitatively.

关键词： Measurement Deep learning computer vision Distortion Cameras Robustness pattern recognition

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Spatio-temporal Contrastive Domain Adaptation for Action recognition

Spatio-temporal Contrastive Domain Adaptation for Action Rec...

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

作者： Song, Xiaolin Zhao, Sicheng Yang, Jingyu Yue, Huanjing Xu, Pengfei Hu, Runbo Chai, Hua Tianjin Univ Tianjin Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA Didi Chuxing Beijing Peoples R China

ISBN: (纸本)9781665445092

Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition.

关键词： Measurement Bridges Adaptation models computer vision Benchmark testing Data models pattern recognition

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