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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是201-210 订阅

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OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

OutfitGAN: Learning Compatible Items for Generative Fashion ...

引用

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

作者： Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

关键词： Industries computer vision conferences Machine learning Generative adversarial networks pattern recognition

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Key Point-Based Driver Activity recognition

Key Point-Based Driver Activity Recognition

引用

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

作者： Vats, Arpita Anastasiu, David C. Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Pose estimation Activity recognition Predictive models Feature extraction

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Compositional Mixture Representations for vision and Text

Compositional Mixture Representations for Vision and Text

引用

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

作者： Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

关键词： Representation learning Visualization Computational modeling Semantics Image retrieval Employment Object detection

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

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

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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Can domain adaptation make object recognition work for everyone?

Can domain adaptation make object recognition work for every...

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

作者： Prabhu, Viraj Selvaraju, Ramprasaath R. Hoffman, Judy Naik, Nikhil Georgia Tech Atlanta GA 30332 USA Artera AI Berkeley CA USA Salesforce Res Washington DC USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.

关键词： Geography computer vision Adaptation models Visualization Image recognition Computational modeling conferences

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AAFormer: A Multi-Modal Transformer Network for Aerial Agricultural Images

AAFormer: A Multi-Modal Transformer Network for Aerial Agric...

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

作者： Shen, Yao Wang, Lei Jin, Yue China Pacific Insurance Grp Co Ltd Shanghai Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic segmentation such as Fully Convolutional Networks can extract rich semantic features, but are difficult to exploit the long-range information. Recently, vision Transformer architectures have made outstanding performances in image segmentation tasks, but transformer-based models have not been fully explored in the field of ***, we propose a novel architecture called Agricultural Aerial Transformer (AAFormer) to solve the semantic segmentation of aerial farmland images. We adopt Mix Transformer (MiT) in the encoder stage to enhance the ability of field anomaly pattern recognition and leverage the Squeeze-and-Excitation (SE) module in the decoder stage to improve the effectiveness of key channels. The boundary maps of farmland are introduced into the decoder. Evaluated on the Agriculture-vision validation set, the mIoU of our proposed model reaches 45.44%.

关键词： Image segmentation Image recognition conferences Semantics computer architecture Transformers Feature extraction

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Rethinking Supervised Depth Estimation for 360° Panoramic Imagery

Rethinking Supervised Depth Estimation for 360° Panoramic I...

引用

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

作者： He, Lu Jian, Bing Wen, Yangming Zhu, Haichao Liu, Kelin Feng, Weiwei Liu, Shan Tencent Amer Palo Alto CA 94306 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a single 360 degrees panorama image is a difficult task. It is an ill-posed problem to estimate depth maps from an RGB panorama image due to the intrinsic scale ambiguity issue. To mitigate the scale inconsistency issue in the ground truth depth map, we propose a simple yet effective method to normalize the depth data based on estimated camera height. In addition, we design a multiple head planar-guided depth network, to provide more geometric constraints for depth estimation. Experimental results show that our relative depth estimation task is more accurate than the absolute depth estimation task, and our proposed model produces state-of-the-art performance on both Matterport3D and Stanford2D3D datasets.

关键词： Measurement computer vision Head conferences Estimation Data models pattern recognition

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Towards Explaining Image-Based Distribution Shifts

Towards Explaining Image-Based Distribution Shifts

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

作者： Kulinski, Sean Inouye, David I. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding such distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on either natively handling distribution shift (e.g., Domain Generalization) or merely detecting a shift while assuming any detected shift can be understood and handled appropriately by a human operator. For the latter, we hope to aid in these manual mitigation tasks by explaining the distribution shift to an operator. To this end, we suggest two methods: providing a set of interpretable mappings from the original distribution to the shifted one or providing a set of distributional counterfactual examples. We provide preliminary experiments on these two methods, and discuss important concepts and challenges for moving towards a better understanding of image-based distribution shifts.

关键词： computer vision conferences pattern recognition Task analysis

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Rank in Style: A Ranking-based Approach to Find Interpretable Directions

Rank in Style: A Ranking-based Approach to Find Interpretabl...

引用

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

作者： Kocasari, Umut Zaman, Kerem Tiftikci, Mert Simsar, Enis Yanardag, Pinar Bogazici Univ Istanbul Turkey TUM Munich Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent work such as StyleCLIP aims to harness the power of CLIP embeddings for controlled manipulations. Although these models are capable of manipulating images based on a text prompt, the success of the manipulation often depends on careful selection of the appropriate text for the desired manipulation. This limitation makes it particularly difficult to perform text-based manipulations in domains where the user lacks expertise, such as fashion. To address this problem, we propose a method for automatically determining the most successful and relevant text-based edits using a pre-trained StyleGAN model. Our approach consists of a novel mechanism that uses CLIP to guide beam-search decoding, and a ranking method that identifies the most relevant and successful edits based on a list of keywords. We also demonstrate the capabilities of our framework in several domains, including fashion.

关键词： computer vision Image synthesis conferences Search problems pattern recognition Decoding

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Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

Conditioned and composed image retrieval combining and parti...

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

作者： Baldrati, Alberto Bertini, Marco Uricchio, Tiberio Del Bimbo, Alberto Univ Firenze MICC Florence Italy Univ Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present an approach for conditioned and composed image retrieval based on CLIP features. In this extension of content-based image retrieval (CBIR) an image is combined with a text that provides information regarding user intentions, and is relevant for application domains like e-commerce. The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder. Then in a second training stage we learn a more complex combiner network that merges visual and textual features. Contrastive learning is used in both stages. The proposed approach obtains state-of-the-art performance for conditioned CBIR on the FashionIQ dataset and for composed CBIR on the more recent CIRR dataset.

关键词： Training Visualization computer vision conferences Image retrieval Solids pattern recognition

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