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共 11891 条记录，以下是1601-1610 订阅

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Text-Guided Face recognition using Multi-Granularity Cross-Modal Contrastive Learning

Text-Guided Face Recognition using Multi-Granularity Cross-M...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hasan, Md Mahedi Sami, Shoaib Meraj Nasrabadi, Nasser West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9798350318920;9798350318937

State-of-the-art face recognition (FR) models often experience a significant performance drop when dealing with facial images in surveillance scenarios where images are in low quality and often corrupted with noise. Leveraging facial characteristics, such as freckles, scars, gender, and ethnicity, becomes highly beneficial in improving FR performance in such scenarios. In this paper, we introduce text-guided face recognition (TGFR) to analyze the impact of integrating facial attributes in the form of natural language descriptions. We hypothesize that adding semantic information into the loop can significantly improve the image understanding capability of an FR algorithm compared to other soft biometrics. However, learning a discriminative joint embedding within the multi-modal space poses a considerable challenge due to the semantic gap in the unaligned image-text representations, along with the complexities arising from ambiguous and incoherent textual descriptions of the face. To address these challenges, we introduce a face-caption alignment module (FCAM), which incorporates cross-modal contrastive losses across multiple granularities to maximize the mutual information between local and global features of the face-caption pair. Within FCAM, we refine both facial and textual features for learning aligned and discriminative features. We also design a face-caption fusion module (FCFM) that applies fine-grained interactions and coarse-grained associations among cross-modal features. Through extensive experiments conducted on three face-caption datasets, proposed TGFR demonstrates remarkable improvements, particularly on low-quality images, over existing FR models and outperforms other related methods and benchmarks.

关键词： Algorithms Algorithms Biometrics body pose face gesture vision + language and/or other modalities

来源：评论

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Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models

Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior...

引用

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

作者： Xu, Jiale Wang, Xintao Cheng, Weihao Cao, Yan-Pei Shan, Ying Qie, Xiaohu Gao, Shenghua ARC Lab Brisbane Qld Australia Tencent PCG Shenzhen Peoples R China ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom A Shanghai Peoples R China

ISBN: (纸本)9798350301298

Recent CLIP-guided 3D optimization methods, such as DreamFields [19] and PureCLIPNeRF [24], have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch training and random initialization without prior knowledge, these methods often fail to generate accurate and faithful 3D structures that conform to the input text. In this paper, we make the first attempt to introduce explicit 3D shape priors into the CLIP-guided 3D optimization process. Specifically, we first generate a high-quality 3D shape from the input text in the text-to-shape stage as a 3D shape prior. We then use it as the initialization of a neural radiance field and optimize it with the full prompt. To address the challenging text-to-shape generation task, we present a simple yet effective approach that directly bridges the text and image modalities with a powerful text-to-image diffusion model. To narrow the style domain gap between the images synthesized by the text-to-image diffusion model and shape renderings used to train the image-to-shape generator, we further propose to jointly optimize a learnable text prompt and fine-tune the text-to-image diffusion model for rendering-style image generation. Our method, Dream3D, is capable of generating imaginative 3D content with superior visual quality and shape accuracy compared to state-of-the-art methods. Our project page is at https://***/dream3d/.

关键词： and reasoning language vision

来源：评论

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Hyperbolic Image Segmentation

Hyperbolic Image Segmentation

引用

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

作者： Atigh, Mina Ghadimi Schoep, Julian Acar, Erman Van Noord, Nanne Mettes, Pascal Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.

关键词： Manifolds Image segmentation Uncertainty Semantics Machine learning pattern recognition Task analysis

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Critical Gap Between Generalization Error and Empirical Error in Active Learning

Critical Gap Between Generalization Error and Empirical Erro...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kanebako, Yusuke Ricoh Co Ltd Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Conventional research papers on Active Learning (AL) have conducted evaluations based on the assumption that a large amount of annotated data is available for evaluating model performance apart from the data selected by AL. This evaluation method is not realistic for the setting where AL learns models with few annotation costs. If a large amount of annotated data is available, it should be used for both evaluation and training, not only for evaluation. Therefore, in a realistic model construction using AL, generalization error in the actual production environment should be estimated by cross-validation only using the data selected by AL. However, the data selected by AL tend to be a biased dataset because the data are selected based on some criteria. Therefore, there is a gap between the actual generalization error and the empirical error when conducting cross-validation on the AL-selected data. In addition, if validation is performed using only the selected dataset by AL, it is possible to fail to realize this fatal gap. In this paper, we show that cross-validation using selected data in conventional AL methods either overestimate or underestimate model performance. As a result, we show a significant difference between generalization error and empirical error from cross-validation.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Pruning from Scratch via Shared Pruning Module and Nuclear norm-based Regularization

Pruning from Scratch via Shared Pruning Module and Nuclear n...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Lee, Donghyeon Leek, Eunho Hwang, Youngbae Chungbuk Natl Univ Dept Intelligent Syst & Robot Cheongju South Korea

ISBN: (纸本)9798350318920;9798350318937

Most pruning methods focus on determining redundant channels from the pre-trained model. However, they overlook the cost of training large networks and the significance of selecting channels for effective reconfiguration. In this paper, we present a "pruning from scratch" framework that considers reconfiguration and expression capacity. Our Shared Pruning Module (SPM) handles a channel alignment problem in residual blocks for lossless reconfiguration after pruning. Moreover, we introduce nuclear norm-based regularization to preserve the representability of large networks during the pruning process. By combining it with MACs-based regularization, we achieve an efficient and powerful pruned network while compressing towards target MACs. The experimental results demonstrate that our method prunes redundant channels effectively to enhance representation capacity of the network. Our approach compresses ResNet50 on ImageNet without requiring additional resources, achieving a top-1 accuracy of 75.25% with only 41% of the original model's MACs. Code is available at https://***/jsleeg98/NuSPM.

关键词： Algorithms Applications Embedded sensing / real-time techniques Image recognition and understanding

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Weakly-Supervised Representation Learning for Video Alignment and Analysis

Weakly-Supervised Representation Learning for Video Alignmen...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Bar-Shalom, Guy Leifman, George Elad, Michael Verily Dallas TX 75019 USA Technion Israel Inst Technol Dept Comp Sci Haifa Israel

ISBN: (纸本)9798350318920;9798350318937

Many tasks in video analysis and understanding boil down to the need for frame-based feature learning, aiming to encapsulate the relevant visual content so as to enable simpler and easier subsequent processing. While supervised strategies for this learning task can be envisioned, self and weakly-supervised alternatives are preferred due to the difficulties in getting labeled data. This paper introduces LR-Prop - a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category. The proposed approach uses a transformer encoder for extracting frame-level features, and employs the DTW algorithm within the training iterations in order to identify the alignment path between video pairs. Through a process referred to as "pair-wise position propagation", the probability distributions of these correspondences per location are matched with the similarity of the frame-level features via KL-divergence minimization. The proposed algorithm uses also a regularized SoftDTW loss for better tuning the learned features. Our novel representation learning paradigm consistently outperforms the state of the art on temporal alignment tasks, establishing a new performance bar over several downstream video analysis applications.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures Video recognition and understanding

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Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior

Multi-view 3D Object Reconstruction and Uncertainty Modellin...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Liao, Ziwei Waslander, Steven L. Univ Toronto Robot Inst Toronto ON Canada Univ Toronto Inst Aerosp Study Toronto ON Canada

ISBN: (纸本)9798350318920;9798350318937

3D object reconstruction is important for semantic scene understanding. It is challenging to reconstruct detailed 3D shapes from monocular images directly due to a lack of depth information, occlusion and noise. Most current methods generate deterministic object models without any awareness of the uncertainty of the reconstruction. We tackle this problem by leveraging a neural object representation which learns an object shape distribution from large dataset of 3d object models and maps it into a latent space. We propose a method to model uncertainty as part of the representation and define an uncertainty-aware encoder which generates latent codes with uncertainty directly from individual input images. Further, we propose a method to propagate the uncertainty in the latent code to SDF values and generate a 3d object mesh with local uncertainty for each mesh component. Finally, we propose an incremental fusion method under a Bayesian framework to fuse the latent codes from multi-view observations. We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty-based fusion to improve 3D object reconstruction accuracy.

关键词： 3D 3D computer vision Algorithms Algorithms Algorithms etc. Generative models for image Image recognition and understanding video

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LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration

LatentDR: Improving Model Generalization Through Sample-Awar...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Liu, Ran Khose, Sahil Xiao, Jingyun Sathidevi, Lakshmi Ramnath, Keerthan Kira, Zsolt Dyer, Eva L. Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350318920;9798350318937

Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure. Our approach first degrades the samples stochastically in the latent space, mapping them to augmented labels, and then restores the samples from their corrupted versions during training. This process confuses the classifier in the degradation step and restores the overall class distribution of the original samples, promoting diverse intra-class/crossdomain variability. We extensively evaluate our approach on a diverse set of datasets and tasks, including domain generalization benchmarks and medical imaging datasets with strong domain shift, where we show our approach achieves significant improvements over existing methods for latent space augmentation. We further show that our method can be flexibly adapted to long-tail recognition tasks, demonstrating its versatility in building more generalizable models. https://***/nerdslab/LatentDR.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Image recognition and understanding Machine learning architectures

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Out-of-Distribution Detection with Logical Reasoning

Out-of-Distribution Detection with Logical Reasoning

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kirchheim, Konstantin Gonschorek, Tim Ortmeier, Frank Otto von Guericke Univ Dept Comp Sci Magdeburg Germany

ISBN: (纸本)9798350318920;9798350318937

Machine Learning models often only generalize reliably to samples from the training distribution. Consequentially, detecting when input data is out-of-distribution (OOD) is crucial, especially in safety-critical applications. Current OOD detection methods, however, tend to be domain agnostic and often fail to incorporate valuable prior knowledge about the structure of the training distribution. To address this limitation, we introduce a novel, hybrid OOD detection algorithm that combines a deep learning-based perception system with a first-order logic-based knowledge representation. A logical reasoning system uses this knowledge base at run-time to infer whether inputs are consistent with prior knowledge about the training distribution. In contrast to purely neural systems, the structured knowledge representation allows humans to inspect and modify the rules that govern the OOD detectors' behavior. This not only enhances performance but also fosters a level of explainability that is particularly beneficial in safety-critical contexts. We demonstrate the effectiveness of our method through experiments on several datasets and discuss advantages and limitations. Our code is available online.(1)

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information

HalluciDet: Hallucinating RGB Modality for Person Detection ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Medeiros, Heitor Rapela Pena, Fidel A. Guerrero Aminbeidokhti, Masih Dubail, Thomas Granger, Eric Pedersoli, Marco ETS Montreal Dept Syst Engn LIVIA Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the same distribution as the target domain. Given a cross-modal application, such as pedestrian detection from aerial images, with a considerable shift in data distribution between infrared (IR) to visible (RGB) images, a translation focused on generation might lead to poor performance as the loss focuses on irrelevant details for the task. In this paper, we propose HalluciDet, an IR-RGB image translation model for object detection. Instead of focusing on reconstructing the original image on the IR modality, it seeks to reduce the detection loss of an RGB detector, and therefore avoids the need to access RGB data. This model produces a new image representation that enhances objects of interest in the scene and greatly improves detection performance. We empirically compare our approach against state-of-the-art methods for image translation and for fine-tuning on IR, and show that our HalluciDet improves detection accuracy in most cases by exploiting the privileged information encoded in a pre-trained RGB detector. Code: https://***/heitorrapela/HalluciDet.

关键词： 3D Algorithms Algorithms Algorithms and algorithms etc. formulations Generative models for image Image recognition and understanding Machine learning architectures video

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