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共 21179 条记录，以下是4971-4980 订阅

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Locate then Segment: A Strong Pipeline for Referring Image Segmentation

Locate then Segment: A Strong Pipeline for Referring Image S...

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

作者： Jing, Ya Kong, Tao Wang, Wei Wang, Liang Li, Lei Tan, Tieniu Chinese Acad Sci CASIA Ctr Res Intelligent Percept & Comp CRIPAC Natl Lab Pattern Recognit NLPR Inst Automat Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature interaction mechanism to fuse the visuallinguistic features to directly generate the final segmentation mask without explicitly modeling the localization information of the referent instances. To tackle these problems, we view this task from another perspective by decoupling it into a "Locate-Then-Segment" (LTS) scheme. Given a language expression, people generally first perform attention to the corresponding target image regions, then generate a fine segmentation mask about the object based on its context. The LTS first extracts and fuses both visual and textual features to get a cross-modal representation, then applies a cross-model interaction on the visual-textual features to locate the referred object with position prior, and finally generates the segmentation result with a light-weight segmentation network. Our LTS is simple but surprisingly effective. On three popular benchmark datasets, the LTS outperforms all the previous state-of-the-arts methods by a large margin (e.g., +3.2% on RefCOCO+ and +3.4% on RefCOCOg). In addition, our model is more interpretable with explicitly locating the object, which is also proved by visualization experiments. We believe this framework is promising to serve as a strong baseline for referring image segmentation.

关键词： Location awareness Image segmentation Visualization Fuses Pipelines Object segmentation Feature extraction

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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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A Generative Approach for Wikipedia-Scale Visual Entity recognition

A Generative Approach for Wikipedia-Scale Visual Entity Reco...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Mathilde Caron Ahmet Iscen Alireza Fathi Cordelia Schmid Google Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual-encoder models (e.g. CLIP), where all the entity names and query images are embedded into a unified space, paving the way for an approximate kNN search. Alternatively, it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast, we introduce a novel Generative Entity recognition (GER)framework, which given an input image learns to auto-regressively decode a semantic and discriminative “code” identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm, showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning, dual-encoder, visual matching and hier-archical classification baselines, affirming its advantage in tackling the complexities of web-scale recognition.

关键词： Visualization Codes Image recognition Target recognition Ovens Semantics Encyclopedias

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Fingerprint Liveliness Detection using Stacked Ensemble and Transfer Learning Technique

Fingerprint Liveliness Detection using Stacked Ensemble and ...

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2024 ieee International conference on computer vision and Machine Intelligence, CVMI 2024

作者： Kumari, Vidya Shekar, B.H. Mangalore University Department of Computer Science Mangaluru India

ISBN: (纸本)9798350376876

The effectiveness and dependability of fingerprint recognition systems make them popular for biometric authentication. This research addresses the challenge of distinguishing between real and spoof fingerprint images using a comprehensive approach that integrates data preprocessing, data augmentation, feature extraction with pretrained models and stacked classifiers. The study begins with rigorous data preprocessing techniques to enhance the quality and consistency of the fingerprint images. Subsequently, data augmentation is employed to increase the diversity of the training dataset, enabling better generalization and robustness of the classification model. Feature extraction is then performed using pretrained convolutional neural network models, namely ResNet50, VGG16, and DenseNet201, to capture discriminative features from the fingerprint *** classifiers leverage the complementary strengths of multiple base classifiers, such as Random Forest, Extra Trees, and Support Vector Classifier, to improve classification performance. The proposed ensemble learning approach achieves an impressive accuracy of 9 9. 3 0 % in distinguishing between real and spoof fingerprint images, demonstrating its effectiveness in enhancing biometric security systems. © 2024 ieee.

关键词： Convolutional neural networks

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Structured Gradient-Based Interpretations via Norm-Regularized Adversarial Training

Structured Gradient-Based Interpretations via Norm-Regulariz...

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conference on computer vision and pattern recognition (cvpr)

作者： Shizhan Gong Qi Dou Farzan Farnia The Chinese University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the simple gradient and integrated gradient algorithms, often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A frequently used approach to inducing sparsity structures into gradient-based saliency maps is to alter the simple gradient scheme using sparsification or norm-based regularization. A drawback with such post-processing methods is their frequently-observed significant loss in fidelity to the original simple gradient map. In this work, we propose to apply adversarial training as an inprocessing scheme to train neural networks with structured simple gradient maps. We show a duality relation between the regularized norms of the adversarial perturbations and gradient-based maps, based on which we design adversarial training loss functions promoting sparsity and group-sparsity properties in simple gradient maps. We present several numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets 1 1 The paper's code is available at: https://***/peterant330/AdvGrad.

关键词： Training computer vision Perturbation methods Benchmark testing Stability analysis Robustness pattern recognition

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Learning Compositional Radiance Fields of Dynamic Human Heads

Learning Compositional Radiance Fields of Dynamic Human Head...

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

作者： Wang, Ziyan Bagautdinov, Timur Lombardi, Stephen Simon, Tomas Saragih, Jason Hodgins, Jessica Zollhofer, Michael Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Menlo Pk CA USA Facebook Real Labs Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Photorealistic rendering of dynamic humans is an important capability for telepresence systems, virtual shopping, special effects in movies, and interactive experiences such as games. Recently, neural rendering methods have been developed to create high-fidelity models of humans and objects. Some of these methods do not produce results with high-enough fidelity for driveable human models (Neural Volumes) whereas others have extremely long rendering times (NeRF). We propose a novel compositional 3D representation that combines the best of previous methods to produce both higher-resolution and faster results. Our representation bridges the gap between discrete and continuous volumetric representations by combining a coarse 3D-structure-aware grid of animation codes with a continuous learned scene function that maps every position and its corresponding local animation code to a view-dependent emitted radiance and local volume density. Differentiable volume rendering is employed to compute photo-realistic novel views of the human head and upper body as well as to train our novel representation end-to-end using only 2D supervision. In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code. Our approach achieves state-of-the-art results for synthesizing novel views of dynamic human heads and the upper body. See our project page' for more results.

关键词： Interpolation Codes Telepresence Three-dimensional displays Aerospace electronics Rendering (computer graphics) Animation

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MoST: Motion Style Transformer Between Diverse Action Contents

MoST: Motion Style Transformer Between Diverse Action Conten...

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conference on computer vision and pattern recognition (cvpr)

作者： Boeun Kim Jungho Kim Hyung Jin Chang Jin Young Choi Korea Electronics Technology Institute Seoul National University University of Birmingham

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While existing motion style transfer methods are effective between two motions with identical content, their performance significantly diminishes when transferring style between motions with different contents. This challenge lies in the lack of clear separation between content and style of a motion. To tackle this challenge, we propose a novel motion style transformer that effectively disentangles style from content and generates a plausible motion with transferred style from a source motion. Our distinctive approach to achieving the goal of disentanglement is twofold: (1) a new architecture for motion style transformer with 'part-attentive style modulator across body parts' and ‘Siamese encoders that encode style and content features separately’; (2) style disentanglement loss. Our method outperforms existing methods and demonstrates exceptionally high quality, particularly in motion pairs with different contents, without the need for heuristic post-processing. Codes are available at https://***/Boeun-Kim/MoST.

关键词： Computational modeling Data acquisition Modulation computer architecture Transformers Data models pattern recognition

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Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning

Patchwise Generative ConvNet: Training Energy-Based Models f...

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

作者： Zheng, Zilong Xie, Jianwen Li, Ping Univ Calif Los Angeles Los Angeles CA 90095 USA Baidu Res Cognit Comp Lab Bellevue WA USA

ISBN: (纸本)9781665445092

Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the internal distribution of patches within the image without relying on external training data. Different from prior works that model such a distribution implicitly with a top-down latent variable model (e.g., generator), this paper proposes to explicitly represent the statistical distribution within a single natural image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Meanwhile, a coarse-to-fine sequential training and sampling strategy is presented to train the model efficiently. Besides learning to generate random samples from white noise, the model can learn in parallel with a self-supervised task (e.g., recover the input image from its corrupted version), which can further improve the descriptive power of the learned model. The proposed model is simple and natural in that it does not require an auxiliary model (e.g., discriminator) to assist the training. Besides, it also unifies internal statistics learning and image generation in a single framework. Experimental results presented on various image generation and manipulation tasks, including super-resolution, image editing, harmonization, style transfer, etc, have demonstrated the effectiveness of our model for internal learning.

关键词： Training Image recognition Image synthesis Computational modeling Superresolution Training data Statistical distributions

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Perceptual Loss for Robust Unsupervised Homography Estimation

Perceptual Loss for Robust Unsupervised Homography Estimatio...

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

作者： Koguciuk, Daniel Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (纸本)9781665448994

Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimizes the distance in the feature space between the warped image from the source viewpoint and the corresponding image from the target viewpoint. Since we use a fixed pre-trained feature extractor and the only learnable component of our framework is the homography network, we effectively decouple the homography estimation from representation learning. We use an additional photometric distortion step in the synthetic COCO dataset generation to better represent the illumination variation of the real-world scenarios. We show that biHomE achieves state-of-the-art performance on synthetic COCO dataset, which is also comparable or better compared to supervised approaches. Furthermore, the empirical results demonstrate the robustness of our approach to illumination variation compared to existing methods.

关键词： Degradation computer vision conferences Estimation Lighting Feature extraction Distortion

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Blur, Noise, and Compression Robust Generative Adversarial Networks

Blur, Noise, and Compression Robust Generative Adversarial N...

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

作者： Kaneko, Takuhiro Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) have gained considerable attention owing to their ability to reproduce images. However, they can recreate training images faithfully despite image degradation in the form of blur, noise, and compression, generating similarly degraded images. To solve this problem, the recently proposed noise robust GAN (NR-GAN) provides a partial solution by demonstrating the ability to learn a clean image generator directly from noisy images using a two-generator model comprising image and noise generators. However, its application is limited to noise, which is relatively easy to decompose owing to its additive and reversible characteristics, and its application to irreversible image degradation, in the form of blur, compression, and combination of all, remains a challenge. To address these problems, we propose blur, noise, and compression robust GAN (BNCR-GAN) that can learn a clean image generator directly from degraded images without knowledge of degradation parameters (e.g., blur kernel types, noise amounts, or quality factor values). Inspired by NR-GAN, BNCR-GAN uses a multiple-generator model composed of image, blur-kernel, noise, and quality-factor generators. However, in contrast to NR-GAN, to address irreversible characteristics, we introduce masking architectures adjusting degradation strength values in a data-driven manner using bypasses before and after degradation. Furthermore, to suppress uncertainty caused by the combination of blur, noise, and compression, we introduce adaptive consistency losses imposing consistency between irreversible degradation processes according to the degradation strengths. We demonstrate the effectiveness of BNCR-GAN through large-scale comparative studies on CIFAR-10 and a generality analysis on FFHQ. In addition, we demonstrate the applicability of BNCR-GAN in image restoration.

关键词： Degradation Training Adaptation models Image coding Uncertainty Computational modeling computer architecture

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