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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是631-640 订阅

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Scalable and Explainable Outfit Generation

Scalable and Explainable Outfit Generation

引用

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

作者： Lorbert, Alexander Neiman, David Poznanski, Arik Oks, Eduard Davis, Larry Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665448994

We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category to a visual descriptor output. This output is then fed into nearest-neighbor-based visual search, which, during training, is learned via triplet loss and mini-batch retrievals. At inference time, we use a beam search with a desired outfit composition to generate outfits at scale. Moreover, the attention networks provide a diagnostic look into the recommendation process, serving as a fashion-based sanity check.

关键词： Training Visualization computer vision conferences pattern recognition

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

引用

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

Training Rare Object Detection in Satellite Imagery with Syn...

引用

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

作者： Martinson, Eric Furlong, Bridget Gillies, Andy Soar Technol 3600 Green StSte 600 Ann Arbor MI 48105 USA

ISBN: (纸本)9781665448994

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.

关键词： Training Solid modeling computer vision Analytical models Three-dimensional displays Satellites Generative adversarial networks

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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Clas...

引用

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

作者： Conde, Marcos, V Turgutlu, Kerem Univ Valladolid Valladolid Spain Adobe Mountain View CA USA

ISBN: (纸本)9781665448994

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. In this work, we use CLIP (Contrastive Language-Image Pre-Training) [12] for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels. Model's zero-shot capability allows predicting the most relevant natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset [20], which we consider the largest annotated artwork dataset. Our code and models will be available at https://***/KeremTurgutlu/clip_art

关键词： Training computer vision Art Image recognition Databases Natural languages Neural networks

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Omnimatte: Associating Objects and Their Effects in Video

Omnimatte: Associating Objects and Their Effects in Video

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

作者： Lu, Erika Cole, Forrester Dekel, Tali Zisserman, Andrew Freeman, William T. Rubinstein, Michael Google Res Mountain View CA 94043 USA Univ Oxford Oxford England Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

computer vision is increasingly effective at segmenting objects in images and videos;however, scene effects related to the objects-shadows, reflections, generated smoke, etc.-are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject-an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic-it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.

关键词： Training Image segmentation computer vision Color Visual effects Reflection pattern recognition

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Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Deep Graphics Encoder for Real-Time Video Makeup Synthesis f...

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

作者： Kips, Robin Jiang, Ruowei Ba, Sileye Phung, Edmund Aarabi, Parham Gori, Pietro Perrot, Matthieu Bloch, Isabelle LOreal Res & Innovat Clichy France Inst Polytech Paris Telecom Paris LTCI Paris France Modiface Toronto ON Canada Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781665448994

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.

关键词： computer vision conferences Estimation Streaming media Rendering (computer graphics) Real-time systems pattern recognition

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Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

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

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

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Shadow Removal with Paired and Unpaired Learning

Shadow Removal with Paired and Unpaired Learning

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

作者： Vasluianu, Florin-Alexandru Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.

关键词： Training computer vision conferences Image restoration pattern recognition Task analysis Light sources

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

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

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

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

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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