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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是101-110 订阅

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

引用

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

作者： Chen, Xiangyu Liu, Jing Wang, Ye Wang, Pu (Perry) Brand, Matthew Wang, Guanghui Koike-Akino, Toshiaki Univ Kansas Lawrence KS 66045 USA Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Toronto Metropolitan Univ Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350365474

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： low-rank adaptation parameter efficiency

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Human Action Adverb recognition: ADHA Dataset and A Three-Stream Hybrid Model 31

Human Action Adverb Recognition: ADHA Dataset and A Three-St...

引用

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

作者： Pang, Bo Zha, Kaiwen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce the first benchmark for a new problem - recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labelling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results reveal that such methods are unsatisfactory. Furthermore, we propose a novel three-stream hybrid model to tackle the HAA problem, which achieves better performances and receives relatively promising results.

关键词： Image recognition Mood Emotion recognition Sports Benchmark testing Face pattern recognition

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Towards Detailed Characteristic-Preserving Virtual Try-On

Towards Detailed Characteristic-Preserving Virtual Try-On

引用

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

作者： Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective method to better preserve details of the clothing and person by introducing an additional fitting step after geometric warping. This minimal modification enables disentangling representations of the clothing from the wearer, hence we are able to preserve the wearer-agnostic structure and details of the clothing, to fit a garment naturally to a variety of poses and body shapes. Moreover, we propose a novel evaluation framework applicable to any metric, to better reflect the semantics of clothes fitting. From extensive experiments, we empirically verify that the proposed method not only learns to disentangle clothing from the wearer, but also preserves details of the clothing on the try-on results.

关键词： Measurement computer vision Shape conferences Clothing Fitting Semantics

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Fast-n-Squeeze: towards real-time spectral reconstruction from RGB images

Fast-n-Squeeze: towards real-time spectral reconstruction fr...

引用

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

作者： Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336Bldg U14 Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present an efficient method for the reconstruction of multispectral information from RGB images, as part of the NTIRE 2022 Spectral Reconstruction Challenge. Given an input image, our method determines a global RGB-to-spectral linear transformation matrix, based on a search through optimal matrices from training images that share low-level features with the input. The resulting spectral signatures are then adjusted by a global scaling factor, determined through a lightweight SqueezeNet-inspired neural network. By combining the efficiency of linear transformation matrices with the data-driven effectiveness of convolutional neural networks, we are able to achieve superior performance than winners of the previous editions of the challenge.

关键词： Training Measurement Image resolution Neural networks Real-time systems pattern recognition Convolutional neural networks

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Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery

Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting C...

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

作者： Cipolina-Kun, Lucia Caenazzo, Simone Mazzei, Gaston ML Collect San Francisco CA 94117 USA Riskcare Ltd London England Univ Paris Saclay CNRS LISN VENISE Team Orsay France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current state-of-the art models for inpainting of large missing regions. We provide qualitative and quantitative comparison of the performance by CoModGANs, LaMa and GLIDE in inpainting of blurry and missing sections of images. We use Escher's incomplete painting Print Gallery as our test study since it presents several of the challenges commonly present in restorative inpainting.

关键词： Degradation Image resolution Computational modeling conferences Digital art Image restoration pattern recognition

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Fusing Visual Saliency for Material recognition 31

Fusing Visual Saliency for Material Recognition

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

作者： Qi, Lin Xu, Ying Shang, Xiaowei Dong, Junyu Ocean Univ China 238 Songling Rd Qingdao 266100 Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Material recognition is researched in both computer vision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance of material image classification models. We first collected eye-tracking data from human observers and used it to fine-tune a generative adversarial network for saliency prediction (SalGAN). We then fused the predicted saliency map with material images and fed them to CNN models for material classification. The experiment results show that the classification accuracy is improved than those using original images. This indicates that human's visual cues could benefit computational models as priors.

关键词： Computational modeling computer vision Visualization Data models Task analysis Training pattern recognition

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Simultaneous Identification and Tracking of Multiple People using Video and IMUs 32

Simultaneous Identification and Tracking of Multiple People ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Henschel, Roberto von Marcard, Timo Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most modern approaches for multiple people tracking rely on human appearance to exploit similarity between person detections. In this work, we propose an alternative tracking method that does not depend on visual appearance and is still capable to deal with very dynamic motions and long-term occlusions. We make this feasible by: (i) incorporating additional information from body-worn inertial sensors, (ii) designing a neural network to relate person detections to orientation measurements and (iii) formulating a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. We evaluate our approach on several challenging tracking sequences and achieve a very high IDF1 score of 91.2%. We outperform appearance-based baselines in scenarios where appearance is less informative and are on-par in situations with discriminative people appearance.

关键词： computer vision

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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

引用

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

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

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

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

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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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