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

共 30988 条记录，以下是4701-4710 订阅

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Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Ima...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gihyun Kwon Simon Jenni Dingzeyu Li Joon-Young Lee Jong Chul Ye Fabian Caba Heilbron KAIST Adobe

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.

关键词： computer vision Fuses Semantics Text to image Diffusion models pattern recognition Optimization

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Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers

Siamese Natural Language Tracker: Tracking by Natural Langua...

引用

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

作者： Feng, Qi Ablavsky, Vitaly Bai, Qinxun Sclaroff, Stan Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU. Code for this work is available at https: // github. com/ fredfung007/ snlt.

关键词： Visualization computer vision Natural languages Graphics processing units computer architecture Benchmark testing Real-time systems

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Adversarial Score Distillation: When Score Distillation Meets GAN

Adversarial Score Distillation: When Score Distillation Meet...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Min Wei Jingkai Zhou Junyao Sun Xuesong Zhang Beijing University of Posts and Telecommunications South China University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale, manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones. To explain and analyze these issues, we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm, we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization, resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD), which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore, to explore the generalization ability of our paradigm, we extend ASD to the image editing task, which achieves competitive results. The project page and code are at this link.

关键词： computer vision Codes Computational modeling Generative adversarial networks pattern recognition Optimization

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On Training Sketch Recognizers for New Domains

On Training Sketch Recognizers for New Domains

引用

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

作者： Yesilbek, Kemal Tugrul Sezgin, T. Metin Beat Res BV Amsterdam Netherlands Koc Univ Istanbul Turkey

ISBN: (纸本)9781665448994

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature.

关键词： Training Deep learning computer vision Protocols conferences Transfer learning Data collection

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Fine-Grained Visual Attribute Extraction from Fashion Wear

Fine-Grained Visual Attribute Extraction from Fashion Wear

引用

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

作者： Parekh, Viral Shaik, Karimulla Biswas, Soma Chelliah, Muthusamy Flipkart Internet Private Ltd Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Automatically extracting visual attributes for e-commerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.

关键词： Visualization computer vision conferences Footwear computer architecture Neck Data mining

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Person Re-identification using Heterogeneous Local Graph Attention Networks

Person Re-identification using Heterogeneous Local Graph Att...

引用

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

作者： Zhang, Zhong Zhang, Haijia Liu, Shuang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China

ISBN: (纸本)9781665445092

Recently, some methods have focused on learning local relation among parts of pedestrian images for person reidentification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation among parts from different images, which results in incomplete local relation information. In this paper, we propose a novel deep graph model named Heterogeneous Local Graph Attention Networks (HLGAT) to model the inter-local relation and the intra-local relation in the completed local graph, simultaneously. Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features. As for the inter-local relation, we propose the attention regularization loss to constrain the attention weights based on the identities of local features in order to describe the inter-local relation accurately. As for the intra-local relation, we propose to inject the contextual information into the attention weights to consider structure information. Extensive experiments on Market-1501, CUHK03, DukeMTMC-reID and MSMT17 demonstrate that the proposed HLGAT outperforms the state-of-the-art methods.

关键词： computer vision Aggregates pattern recognition Context modeling

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SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field

SurroundSDF: Implicit 3D Scene Understanding Based on Signed...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lizhe Liu Bohua Wang Hongwei Xie Daqi Liu Li Liu Zhiqiang Tian Kuiyuan Yang Bing Wang Xiaomi EV School of Software Engineering Xi'an Jiaotong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-centric 3D environment understanding is both vi-tal and challenging for autonomous driving systems. Re-cently, object-free methods have attracted considerable at-tention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end, in this paper, we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically, we introduce a query-based approach and utilize SDF con-strained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore, considering the absence of precise SDF ground truth, we propose a novel weakly supervised paradigm for SDF, referred to as the Sandwich Eikonal formulation, which emphasizes applying correct and dense constraints on both sides of the surface, thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

关键词： Surface reconstruction computer vision Three-dimensional displays Accuracy Semantics Cameras pattern recognition

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AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

AttentiveNAS: Improving Neural Architecture Search via Atten...

引用

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

作者： Wang, Dilin Li, Meng Gong, Chengyue Chandra, Vikas Facebook Menlo Pk CA 94025 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models. While uniform sampling has been widely used for its simplicity, it is agnostic of the model performance Pareto front, which is the main focus in the search process, and thus, misses opportunities to further improve the model accuracy. In this work, we propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto. We also propose algorithms to efficiently and effectively identify the networks on the Pareto during training. Without extra re-training or post-processing, we can simultaneously obtain a large number of networks across a wide range of FLOPs. Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS, Once-for-All networks and FBNetV3. We also achieve ImageNet accuracy of 80.1% with only 491 MFLOPs. Our training code and pretrained models are available at https://***/facebookresearch/AttentiveNAS.

关键词： Training computer vision Codes computer architecture pattern recognition

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Every Corporation Owns Its Structure: Corporate Credit Rating via Graph Neural Networks 5th

Every Corporation Owns Its Structure: Corporate Credit Ratin...

引用

5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Feng, Bojing Xu, Haonan Xue, Wenfang Xue, Bindang Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beihang Univ Sch Astronaut Beijing Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.

关键词： Corporate credit rating Financial risk Graph neural networks

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Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

Learning View-Disentangled Human Pose Representation by Cont...

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

作者： Zhao, Long Wang, Yuxiao Zhao, Jiaping Yuan, Liangzhe Sun, Jennifer J. Schroff, Florian Adam, Hartwig Peng, Xi Metaxas, Dimitris Liu, Ting Rutgers State Univ New Brunswick NJ 08901 USA Google Res Mountain View CA USA CALTECH Pasadena CA 91125 USA Univ Delaware Newark DE 19716 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. We further propose two regularization terms to ensure disentanglement and smoothness of the learned representations. The resulting pose representations can be used for cross-view action recognition. To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition. This task trains models with actions from only one single viewpoint while models are evaluated on poses captured from all possible viewpoints. We evaluate the learned representations on standard benchmarks for action recognition, and show that (i) CV-MIM performs competitively compared with the state-of-the-art models in the fully-supervised scenarios;(ii) CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting;(iii) and the learned representations can significantly boost the performance when reducing the amount of supervised training data. Our code is made publicly available at https://***/google-research/google-research/tree/master/poem.

关键词： computer vision Codes Training data Benchmark testing Data models pattern recognition Task analysis

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