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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Posterior Promoted GAN with Distribution Discriminator for Unsupervised Image Synthesis

Posterior Promoted GAN with Distribution Discriminator for U...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Xianchao Cheng, Ziyang Zhang, Xiaotong Liu, Han Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China

ISBN: (纸本)9781665445092

Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P(2)GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our framework, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P(2)GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P(2)GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis.

关键词： Training Manifolds Interpolation computer vision Image synthesis Computational modeling Gaussian distribution

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ContactOpt: Optimizing Contact to Improve Grasps

ContactOpt: Optimizing Contact to Improve Grasps

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Grady, Patrick Tang, Chengcheng Twigg, Christopher D. Minh Vo Brahmbhatt, Samarth Kemp, Charles C. Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Redmond WA USA Intel Labs Hillsboro OR USA

ISBN: (纸本)9781665445092

Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differentiable contact model. Notably, our contact model encourages mesh interpenetration to approximate deformable soft tissue in the hand. In our evaluations, our methods result in grasps that better match ground truth contact, have lower kinematic error, and are significantly preferred by human participants. Code and models are available online(1).

关键词： Deformable models computer vision Codes Pose estimation Biological tissues Kinematics Data models

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Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Joint Generative and Contrastive Learning for Unsupervised P...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Hao Wang, Yaohui Lagadec, Benoit Dantcheva, Antitza Bremond, Francois INRIA Le Chesnay France Univ Cote Azur Nice France European Syst Integrat Le Cannet France

ISBN: (纸本)9781665445092

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

关键词： Training computer vision Adaptation models Three-dimensional displays Codes Image synthesis Generative adversarial networks

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ScanFormer: Referring Expression Comprehension by Iteratively Scanning

ScanFormer: Referring Expression Comprehension by Iterativel...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Wei Su Peihan Miao Huanzhang Dou Xi Li College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Zhejiang-Singapore Innovation and Al Joint Research Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely Ref COCO, Ref COCO+, Ref COCO g, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

关键词： Visualization computer vision Accuracy Computational modeling Linguistics pattern recognition Iterative methods

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Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

Fashion IQ: A New Dataset Towards Retrieving Images by Natur...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Hui Gao, Yupeng Guo, Xiaoxiao Al-Halah, Ziad Rennie, Steven Grauman, Kristen Feris, Rogerio MIT IBM Watson AI Lab Cambridge MA 02142 USA IBM Res Armonk NY 10504 USA UT Austin Austin TX USA Pryon New York NY USA

ISBN: (纸本)9781665445092

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialogbased image retrieval. We believe that our dataset will encourage further work on developing more natural and realworld applicable conversational shopping assistants.(1)

关键词： Visualization computer vision Image retrieval Natural languages Clothing Buildings Ontologies

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StyleMix: Separating Content and Style for Enhanced Data Augmentation

StyleMix: Separating Content and Style for Enhanced Data Aug...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hong, Minui Choi, Jinwoo Kim, Gunhee Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but mix or cut-andpaste the images. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair's class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-10, CIFAR-100 and ImageNet datasets show that StyleMix achieves better or comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks.

关键词： Training Deep learning computer vision Head Image color analysis Skin Robustness

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NeuralFusion: Online Depth Fusion in Latent Space

NeuralFusion: Online Depth Fusion in Latent Space

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Weder, Silvan Schonberger, Johannes L. Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is an online process, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers. The source code will be made available at https://***/weders/NeuralFusion.

关键词： Shape Superresolution Neural networks Real-time systems Encoding pattern recognition Task analysis

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MIST: Multiple Instance Spatial Transformer

MIST: Multiple Instance Spatial Transformer

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Angles, Baptiste Jin, Yuhe Kornblith, Simon Tagliasacchi, Andrea Yi, Kwang Moo Univ Victoria Victoria BC Canada Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant K patches, and feeds these patches to a task-specific network - e.g., auto-encoder or classifier - to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurring structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.

关键词： Training Location awareness Transformers Image decomposition pattern recognition Proposals Task analysis

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clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

clDice - a Novel Topology-Preserving Loss Function for Tubul...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shit, Suprosanna Paetzold, Johannes C. Sekuboyina, Anjany Ezhov, Ivan Unger, Alexander Zhylka, Andrey Pluim, Josien P. W. Bauer, Ulrich Menze, Bjoern H. Tech Univ Munich Munich Germany Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781665445092

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic;particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerline-Dice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.

关键词： Training computer vision Three-dimensional displays Network topology Roads Neurons Neural networks

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Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

Reciprocal Landmark Detection and Tracking with Extremely Fe...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lin, Jianzhe Sahebzamani, Ghazal Luong, Christina Dezaki, Fatemeh Taheri Jafari, Mohammad Abolmaesumi, Purang Tsang, Teresa Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Univ British Columbia Dept Med Vancouver BC Canada Univ British Columbia Div Cardiol Vancouver BC Canada

ISBN: (纸本)9781665445092

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels;only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.

关键词： Training Location awareness computer vision Echocardiography Annotations Computational modeling Machine learning

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