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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21010 条记录，以下是1641-1650 订阅

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FastDOG: Fast Discrete Optimization on GPU

FastDOG: Fast Discrete Optimization on GPU

引用

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

作者： Abbas, Ahmed Swoboda, Paul Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a massively parallel Lagrange decomposition method for solving 0-1 integer linear programs occurring in structured prediction. We propose a new iterative update scheme for solving the Lagrangean dual and a perturbation technique for decoding primal solutions. For representing subproblems we follow [40] and use binary decision diagrams (BDDs). Our primal and dual algorithms require little synchronization between subproblems and optimization over BDDs needs only elementary operations without complicated control flow. This allows us to exploit the parallelism offered by GPUs for all components of our method. We present experimental results on combinatorial problems from MAP inference for Markov Random Fields, quadratic assignment and cell tracking for developmental biology. Our highly parallel GPU implementation improves upon the running times of the algorithms from [40] by up to an order of magnitude. In particular, we come close to or outperform some state-of-the-art specialized heuristics while being problem agnostic.

关键词： Perturbation methods Graphics processing units Prototypes Parallel processing Prediction algorithms Inference algorithms pattern recognition

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SPAct: Self-supervised Privacy Preservation for Action recognition

SPAct: Self-supervised Privacy Preservation for Action Recog...

引用

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

作者： Dave, Ishan Rajendrakumar Chen, Chen Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels. Our training framework consists of three main components: anonymization function, self-supervised privacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and maximize the privacy cost function through a contrastive self-supervised loss. Employing existing protocols of knownaction and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. In addition, we introduce a new protocol to evaluate the generalization of learned the anonymization function to novel-action and privacy attributes and show that our self-supervised framework outperforms existing supervised methods.

关键词： Training Privacy Data privacy Visualization computer vision Protocols Self-supervised learning

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Neural Reflectance for Shape Recovery with Shadow Handling

Neural Reflectance for Shape Recovery with Shadow Handling

引用

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

作者： Li, Junxuan Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper aims at recovering the shape of a scene with unknown, non-Lambertian, and possibly spatially-varying surface materials. When the shape of the object is highly complex and that shadows cast on the surface, the task becomes very challenging. To overcome these challenges, we propose a coordinate-based deep MLP (multilayer perceptron) to parameterize both the unknown 3D shape and the unknown reflectance at every surface point. This network is able to leverage the observed photometric variance and shadows on the surface, and recover both surface shape and general non-Lambertian reflectance. We explicitly predict cast shadows, mitigating possible artifacts on these shadowing regions, leading to higher estimation accuracy. Our framework is entirely self-supervised, in the sense that it requires neither ground truth shape nor BRDF Tests on real-world images demonstrate that our method outperform existing methods by a significant margin. Thanks to the small size of the MLP-net, our method is an order of magnitude faster than previous CNN-based methods.

关键词： Reflectivity computer vision Three-dimensional displays Shape Estimation Multilayer perceptrons pattern recognition

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NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning

NinjaDesc: Content-Concealing Visual Descriptors via Adversa...

引用

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

作者： Ng, Tony Kim, Hyo Jin Lee, Vincent T. DeTone, Daniel Yang, Tsun-Yi Shen, Tianwei Ilg, Eddy Balntas, Vassileios Mikolajczyk, Krystian Sweeney, Chris Meta Real Labs Menlo Pk CA 94025 USA Imperial Coll London London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accuracy. We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors. The experimental results demonstrate that the visual descriptors obtained with our method significantly deteriorate the image reconstruction quality with minimal impact on correspondence matching and camera localization performance.

关键词： Representation learning Training Visualization Privacy computer vision Three-dimensional displays Adversarial machine learning

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ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning

ScePT: Scene-consistent, Policy-based Trajectory Predictions...

引用

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

作者： Chen, Yuxiao Ivanovic, Boris Pavone, Marco NVIDIA Res Santa Clara CA 95051 USA Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Trajectory prediction is a critical functionality of autonomous systems that share environments with uncontrolled agents, one prominent example being self-driving vehicles. Currently, most prediction methods do not enforce scene consistency, i.e., there are a substantial amount of self-collisions between predicted trajectories of different agents in the scene. Moreover, many approaches generate individual trajectory predictions per agent instead of joint trajectory predictions of the whole scene, which makes downstream planning difficult. In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction. Experiments on multiple real-world pedestrians and autonomous vehicle datasets show that ScePT matches current state-of-the-art prediction accuracy with significantly improved scene consistency. We also demonstrate ScePT's ability to work with a downstream contingency planner.

关键词： computer vision Contingency management Predictive models Trajectory Planning pattern recognition Autonomous vehicles

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PyMiceTracking: An Open-Source Toolbox For Real-Time Behavioral Neuroscience Experiments

PyMiceTracking: An Open-Source Toolbox For Real-Time Behavio...

引用

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

作者： Menezes, Richardson de Miranda, Aron Maia, Helton Univ Fed Rio Grande do Norte Dept Comp Engn & Automat Natal RN Brazil Brain Inst Natal RN Brazil Univ Fed Rio Grande do Norte Sch Sci & Technol Natal RN Brazil

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The development of computational tools allows the advancement of research in behavioral neuroscience and elevates the limits of experiment design. Many behavioral experiments need to determine the animal's position from its tracking, which is crucial for real-time decision-making and further analysis of experimental data. Modern experimental designs usually generate the recording of a large amount of data, requiring the development of automatic computational tools and intelligent algorithms for timely data acquisition and processing. The proposed tool in this study initially operates with the acquisition of images. Then the animal tracking step begins with background subtraction, followed by the animal contour detection and morphological operations to remove noise in the detected shapes. Finally, in the final stage of the algorithm, the principal components analysis (PCA) is applied in the obtained shape, resulting in the animal's gaze direction.

关键词： Neuroscience Shape Tutorials Real-time systems Behavioral sciences Recording pattern recognition

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Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

Deep Prototypical-Parts Ease Morphological Kidney Stone Iden...

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

作者： Flores-Araiza, Daniel Lopez-Tiro, Francisco El-Beze, Jonathan Hubert, Jacques Gonzalez, Miguel Ruiz, Gilberto Ochoa Daul, Christian Tecnol Monterrey Sch Engn Mexico City DF Mexico CHU Nancy Serv Urol Brabois Nancy France Univ Lorraine CRAN UMR 7039 Nancy France

ISBN: (纸本)9798350302493

Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based methods outperform non-DL methods in terms of accuracy but lack explainability. Despite this trade-off, when it comes to making high-stakes decisions, its important to prioritize understandable computer-Aided Diagnosis (CADx) that suggests a course of action based on reasonable evidence, rather than a model prescribing a course of action. In this proposal, we learn Prototypical Parts (PPs) per kidney stone subtype, which are used by the DL model to generate an output classification. Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable. In addition, we modify global visual characteristics to describe their relevance to the PPs and the sensitivity of our models performance. With this, we provide explanations with additional information at the sample, class and model levels in contrast to previous works. Although our implementations average accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by 1.5%, our models perform 2.8% better on perturbed images with a lower standard deviation, without adversarial training. Thus, Learning PPs has the potential to create more robust DL models. Code at: https://***/DanielF29/Prototipical_Parts

关键词： computer aided diagnosis

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Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness

Smooth-Swap: A Simple Enhancement for Face-Swapping with Smo...

引用

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

作者： Kim, Jiseob Lee, Jihoon Zhang, Byoung-Tak Seoul Natl Univ Seoul South Korea Kakao Brain Seongnam South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called 'Smooth-Swap', which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, Face-Forensics++) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods.

关键词： Training computer vision Shape Face recognition Computational modeling computer architecture Generators

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Human-Object Interaction Detection via Disentangled Transformer

Human-Object Interaction Detection via Disentangled Transfor...

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

作者： Zhou, Desen Liu, Zhichao Wang, Jian Wang, Leshan Hu, Tao Ding, Errui Wang, Jingdong Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions. Existing HOI transformers either adopt a single decoder for triplet prediction, or utilize two parallel decoders to detect individual objects and interactions separately, and compose triplets by a matching process. In contrast, we decouple the triplet prediction into human-object pair detection and interaction classification. Our main motivation is that detecting the human-object instances and classifying interactions accurately needs to learn representations that focus on different regions. To this end, we present Disentangled Transformer, where both encoder and decoder are disentangled to facilitate learning of two subtasks. To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder. Extensive experiments show that our method outperforms prior work on two public HOI benchmarks by a sizeable margin. Code will be available.

关键词： Location awareness Visualization computer vision Codes Benchmark testing Transformers Decoding

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Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks

Exploring Patch-wise Semantic Relation for Contrastive Learn...

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

作者： Jung, Chanyong Kwon, Gihyun Ye, Jong Chul Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency (SRC) regularization along with the decoupled contrastive learning, which utilize the diverse semantics by focusing on the heterogeneous semantics between the image patches of a single image. To further improve the performance, we present a hard negative mining by exploiting the semantic relation. We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. Experimental results confirmed the state-of-art performance of our method in all the three tasks.

关键词： computer vision Image coding Computational modeling Semantics Focusing pattern recognition Task analysis

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