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共 11897 条记录，以下是1731-1740 订阅

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It's Time for Artistic Correspondence in Music and Video

It's Time for Artistic Correspondence in Music and Video

引用

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

作者： Suris, Didac Vondrick, Carl Russell, Bryan Salamon, Justin Columbia Univ New York NY 10027 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capture the high-level concepts that are required to solve the task, we propose modeling the long-term temporal context of both the video and the music signals, using Transformer networks for each modality. Experiments show that this approach strongly outperforms alternatives that do not exploit the temporal context. The combination of our contributions improve retrieval accuracy up to 10x over prior state of the art. This strong improvement allows us to introduce a wide range of analyses and applications. For instance, we can condition music retrieval based on visually defined attributes.

关键词： computer vision Annotations Transformers pattern recognition Multiple signal classification Task analysis Context modeling

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Exploiting Temporal Relations on Radar Perception for Autonomous Driving

Exploiting Temporal Relations on Radar Perception for Autono...

引用

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

作者： Li, Peizhao Wang, Pu Berntorp, Karl Liu, Hongfu Brandeis Univ Waltham MA 02453 USA Mitsubishi Elect Res Labs Cambridge MA USA MERL Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. lb enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches.

关键词： Radar Object detection Radar imaging Sensors pattern recognition Object recognition Object tracking

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From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

From Denoising Training to Test-Time Adaptation: Enhancing D...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wen, Ruxue Yuan, Hangjie Ni, Dong Xiao, Wenbo Wu, Yaoyao Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350318920;9798350318937

In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors. These shifts are particularly pronounced in the most common scenario, which involves only single-source domain data due to privacy concerns. To address this, we draw inspiration from the self-supervised learning paradigm that effectively discourages overfitting to the source domain. We propose the Denoising Y-Net (DeY-Net), a novel approach incorporating an auxiliary denoising decoder into the basic U-Net architecture. The auxiliary decoder aims to perform denoising training, augmenting the domain-invariant representation that facilitates domain generalization. Furthermore, this paradigm provides the potential to utilize unlabeled data. Building upon denoising training, we propose Denoising Test Time Adaptation (DeTTA) that further: (i) adapts the model to the target domain in a sample-wise manner, and (ii) adapts to the noise-corrupted input. Extensive experiments conducted on widely-adopted liver segmentation benchmarks demonstrate significant domain generalization improvements over our baseline and state-of-the-art results compared to other methods. Code is available at https://***/WenRuxue/DeTTA.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow

Detection Defenses: An Empty Promise against Adversarial Pat...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Scheurer, Erik Schmalfuss, Jenny Lis, Alexander Bruhn, Andres Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany

ISBN: (纸本)9798350318920;9798350318937

Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion detection and its downstream applications. Potential remedies are defense strategies that detect and remove adversarial patches, but their influence on the underlying motion prediction has not been investigated. In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions. In particular, we implement defense-aware attacks to investigate whether current defenses are able to withstand attacks that take the defense mechanism into account. Our experiments yield two surprising results: Detect-and-remove defenses do not only lower the optical flow quality on benign scenes, in doing so, they also harm the robustness under patch attacks for all tested optical flow methods except FlowNetC. As currently employed detect-and-remove defenses fail to deliver the promised adversarial robustness for optical flow, they evoke a false sense of security. The code is available at https://***/cv-stuttgart/DetectionDefenses.

关键词： adversarial attack and defense methods Adversarial learning Algorithms Algorithms Video recognition and understanding

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Self-supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics

Self-supervised Deep Image Restoration via Adaptive Stochast...

引用

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

作者： Wang, Weixi Li, Ji Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or unsupervised methods to address the challenges and costs of collecting truth images. Based on the neuralization of a Bayesian estimator of the problem, this paper presents a self-supervised deep learning approach to general image restoration problems. The key ingredient of the neuralized estimator is an adaptive stochastic gradient Langevin dynamics algorithm for efficiently sampling the posterior distribution of network weights. The proposed method is applied on two image restoration problems: compressed sensing and phase retrieval. The experiments on these applications showed that the proposed method not only outperformed existing non-learning and unsupervised solutions in terms of image restoration quality, but also is more computationally efficient.

关键词： Deep learning computer vision Costs Image restoration Computational efficiency pattern recognition Bayes methods

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Deformable Sprites for Unsupervised Video Decomposition

Deformable Sprites for Unsupervised Video Decomposition

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

作者： Ye, Vickie Li, Zhengqi Tucker, Richard Kanazawa, Angjoo Snavely, Noah Univ Calif Berkeley Berkeley CA 94720 USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos.

关键词： Deformable models Training computer vision pattern recognition Internet Sprites (computer) Standards

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Robust Unsupervised Domain Adaptation through Negative-View Regularization

Robust Unsupervised Domain Adaptation through Negative-View ...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Jang, Joonhyeok Lee, Sunhyeok Kim, Seonghak Kim, Jung-un Kim, Seonghyun Kim, Daeshik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea SHINSEGAE I&C Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

In the realm of Unsupervised Domain Adaptation (UDA), vision Transformers (ViTs) have recently demonstrated remarkable adaptability surpassing that of traditional Convolutional Neural Networks (CNNs). Nevertheless, the patch-based structure of ViTs heavily relies on local features within image patches, potentially leading to reduced robustness when confronted with out-of-distribution (OOD) samples. To address this concern, we introduce a novel regularizer tailored specifically for UDA. By leveraging negative views, i.e. target-domain samples applied by negative augmentations, we make the learning process more intricate, thereby preventing models from taking shortcuts in spatial context recognition. We present a novel loss function, rooted in contrastive principles, to effectively distinguish between the negative views and original target samples. By integrating this novel regularizer with existing UDA methodologies, we guide ViTs to prioritize context relationships among local patches, thereby enhancing the robustness of ViTs. Our proposed Negative View-based Contrastive (NVC) regularizer substantially boosts the performance of baseline UDA methods across diverse benchmark datasets. Furthermore, we release new dataset, Retail-71, comprising 71 classes of images commonly encountered in retail stores. Through comprehensive experimentation, we showcase the effectiveness of our approach on traditional benchmarks as well as the novel retail domain. These results substantiate the robust adaptation capabilities of our proposed method. Our method is implemented at our repository.

关键词： Algorithms Algorithms Algorithms and algorithms Datasets and evaluations formulations Image recognition and understanding Machine learning architectures

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SequenceMatch Revisiting the design of weak-strong augmentations for Semi-supervised learning

SequenceMatch Revisiting the design of weak-strong augmentat...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Khanh-Binh Nguyen Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL methods face is the confirmation bias, which occurs when the model is overfitted to the small labeled training dataset and produces overconfident, incorrect predictions. To address this issue, we propose SequenceMatch, an efficient SSL method that utilizes multiple data augmentations. The key element of SequenceMatch is the inclusion of a medium augmentation for unlabeled data. By taking advantage of different augmentations and the consistency constraints between each pair of augmented examples, SequenceMatch helps reduce the divergence between the prediction distribution of the model for weakly and strongly augmented examples. In addition, SequenceMatch defines two different consistency constraints for high and low-confidence predictions. As a result, SequenceMatch is more data-efficient than ReMixMatch, and more time-efficient than both ReMixMatch (x4) and CoMatch (x2) while having higher accuracy. Despite its simplicity, SequenceMatch consistently outperforms prior methods on standard benchmarks, such as CIFAR-10/100, SVHN, and STL-10. It also surpasses prior state-of-the-art methods by a large margin on large-scale datasets such as ImageNet, with a 38.46% error rate. Code is available at https://***/beandkay/SequenceMatch.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation

Eigencontours: Novel Contour Descriptors Based on Low-Rank A...

引用

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

作者： Park, Wonhui Jin, Dongkwon Kim, Chang-Su Korea Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the Al eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more e f ficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningfid performances on instance segmentation datasets.

关键词： Training computer vision Approximation algorithms pattern recognition Matrix decomposition

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A Geometry Loss Combination for 3D Human Pose Estimation

A Geometry Loss Combination for 3D Human Pose Estimation

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Matsune, Ai Hu, Shichen Li, Guangquan Wen, Sihan Zhu, Xiantan Tan, Zhiming Fujitsu R&D Ctr Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Root-relative loss has formed the basis of 3D human pose estimation for many years. However, this point-to-point loss treats every keypoint separately and ignores internal connection information of the human body. This leads to illegal pose prediction, which humans cannot form in the real world. It also suffers from differences in estimation difficulty between keypoints. The farther the keypoint is from the torso, the less accurate it is. To address the above problems, this paper proposes geometry loss combination to utilize the geometric relationship between each keypoint fully. This loss combination consists of three loss functions: root-relative pose, bone length, and body part orientation. The previous two have already been used in prior works. Beyond them, we further develop a loss function called body part orientation loss for local body parts. Intuitively, the human body can be divided into three parts: the head, torso, and limbs. Based on this, we select the corresponding keypoints and create virtual planes for each body part. Experiments with different datasets and models demonstrate that our proposed method improves the prediction accuracy. We also achieve MPJPE of 65.0 on the 3DPW test set, which outperforms state-of-the-art methods.

关键词： 3D computer vision Algorithms Algorithms Video recognition and understanding

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