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检索条件"任意字段=IEEE Workshop on Variational and Level Set Methods in Computer Vision"

共 149 条记录，以下是1-10 订阅

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ieee workshop on variational and level set methods in computer vision

引用

Journal of Visual Communication and Image Representation 2000年第4期11卷 404-404页

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Proceedings - ieee workshop on variational and level set methods in computer vision, VLSM 2001

Proceedings - IEEE Workshop on Variational and Level Set Met...

引用

ieee workshop on variational and level set methods in computer vision, VLSM 2001

ISBN: (纸本)076951278X

The proceedings contain 24 papers. The topics discussed include: a variational approach to multi-modal image matching;optimal mass transport and image registration;on smoothness measures of active contours and surfaces;on affine invariance in the Beltrami framework for vision;combining total variation and wavelet packet approaches for image deburring;stability of image restoration by minimizing regularized objective functions;multiple contour finding and perceptual grouping using minimal paths;affine invariant edge completion with affine geodesics;3D automated segmentation and structural analysis of vascular trees using deformable models;cortex segmentation-a fast variational geometric approach;diffusion-snakes: combining statistical shape knowledge and image information in a variational framework;and a self-referencing level-set method for image reconstruction from sparse Fourier samples.

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Proceedings ieee workshop on variational and level set methods in computer vision

Proceedings IEEE Workshop on Variational and Level Set Metho...

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ieee workshop on variational and level set methods in computer vision

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Optimizing vision-Language Model for Road Crossing Intention Estimation

Optimizing Vision-Language Model for Road Crossing Intention...

引用

ieee workshop on Applications of computer vision (WACV)

作者： Roy Uziel Oded Bialer General Motors Technical Center Israel Ben-Gurion University of the Negev

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Identifying a pedestrian's intention to cross the road is crucial for autonomous driving, as it alerts the system to stop or slow down. However, determining crossing intention from video is challenging due to the need for extracting complex high-level semantics. This paper introduces ClipCross, a novel classification framework optimized to ex-tract high-level semantic features using the vision-language model CLIP for determining crossing intention. Existing CLIP-based methods perform poorly in this task, as CLIP's image and text encoders fail to capture the nuanced se-mantic distinctions between crossing and non-crossing in-tention images. Clip Cross addresses this by optimizing a set of CLIP text embeddings to extract high-level semantic features, which a multi-layer perceptron uses to distinguish between crossing and non-crossing intentions. Clip Cross achieves state-of-the-art performance on crossing intention estimation benchmark datasets: PIE, PSI, and lAAD.

关键词： computer vision Pedestrians Roads Semantics Estimation Benchmark testing Feature extraction Vectors Autonomous vehicles

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Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer

Ordinal Multiple-instance Learning for Ulcerative Colitis Se...

引用

ieee workshop on Applications of computer vision (WACV)

作者： Kaito Shiku Kazuya Nishimura Daiki Suehiro Kiyohito Tanaka Ryoma Bise Kyushu University National Cancer Center Japan Kyoto Second Red Cross Hospital

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Patient-level diagnosis of severity in ulcerative colitis (UC) is common in clinical practice, where the most severe score for a patient is typically recorded as the diagnosis result. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the input was a single image. Thus, these methods can not utilize severity labels recorded in real clinical settings. In this paper, we propose a patient-level severity estimation method by a transformer with selective aggregator tokens, where a severity label is estimated from multiple images taken from a patient, similar to a clinical setting. Our method can effectively aggregate features of severe parts from a set of images captured in each patient, and it facilitates improving the discriminative ability between adjacent severity classes. Experiments demonstrate the effectiveness of the proposed method on two datasets compared with the state-of-the-art MIL methods. Moreover, we evaluated our method using real clinical data and confirmed that our method outperformed the previous image-level methods. The code is publicly available at https://***/Shiku-Kaito/Ordinal-Multiple-instance-Learning-for-Ulcerative-Colitis-Severity-Estimation.

关键词： Learning systems computer vision Codes Annotations Aggregates Estimation Transformers Feature extraction Ulcerative colitis

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CE-VAE: Capsule Enhanced variational AutoEncoder for Underwater Image Enhancement

CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwa...

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ieee workshop on Applications of computer vision (WACV)

作者： Rita Pucci Niki Martinel Naturalis Biodiversity Center Leiden Netherlands University of Udine Udine Italy

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unmanned underwater image analysis for marine monitoring faces two key challenges: (i) degraded image quality due to light attenuation and (ii) hardware storage constraints limiting high-resolution image collection. Existing methods primarily address image enhancement with approaches that hinge on storing the full-size input. In contrast, we introduce the Capsule Enhanced variational AutoEncoder (CE-VAE), a novel architecture designed to efficiently compress and enhance degraded underwater images. Our attention-aware image encoder can project the input image onto a latent space representation while being able to run online on a remote device. The only information that needs to be stored on the device or sent to a beacon is a compressed representation. There is a dual-decoder module that performs offline, full-size enhanced image generation. One branch reconstructs spatial details from the compressed latent space, while the second branch utilizes a capsule-clustering layer to capture entity-level structures and complex spatial relationships. This parallel decoding strategy enables the model to balance fine-detail preservation with context-aware enhancements. CE- VAE achieves state-of-the-art performance in underwater image enhancement on six benchmark datasets, providing up to 3 × higher compression efficiency than existing approaches. Code available at https://***/iN1k1/ce-vae-underwater-image-enhancement.

关键词： Image quality Performance evaluation Image coding Limiting Autoencoders computer architecture Benchmark testing Image enhancement Image reconstruction Monitoring

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Information Theoretic Pruning of Coupled Channels in Deep Neural Networks

Information Theoretic Pruning of Coupled Channels in Deep Ne...

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ieee workshop on Applications of computer vision (WACV)

作者： Peyman Rostami Nilotpal Sinha Nidhaleddine Chenni Anis Kacem Abd El Rahman Shabayek Carl Shneider Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

variational channel pruning approaches have obtained impressive results thanks to their stochastic nature, well established foundation in information theory, and the practically appealing structured sparsity pattern they offer. Despite their success in pruning Plain Networks (PlainNets), their application has faced certain limitations in networks with structurally coupled channels such as ResNets. In such scenarios, not only is it required to prune structurally coupled channels together, but it is also necessary to ensure that the whole coupled group is irrelevant before pruning is applied. This is an under-investigated problem as most existing methods are designed without taking these couplings into account. In this paper, we propose a novel approach based on Information Theoretic Pruning of structurally Coupled Channels (ITPCC) in neural networks. IT-PCC allows for learning the probabilistic distribution of coupled channel set importance and prunes the ones with the least relevant information to the task at hand. Experimental results for image classification on CIFAR10, CI-FAR100, and ImageNet datasets show that the proposed method outperforms the state-of-the-art, more significantly at high compression rates.

关键词： Hands Couplings computer vision Image coding Neural network compression Artificial neural networks Probabilistic logic Information theory Image classification

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variational, Geometric, and level set methods in computer vision - Third International workshop, VLSM 2005, Proceedings

引用

3rd International workshop on variational, Geometric, and level set methods in computer vision, VLSM 2005

ISBN: (纸本)3540293485

The proceedings contain 30 papers. The topics discussed include: a study of non-smooth convex flow decomposition;nonlinear inverse scale space methods for image restoration;towards PDE-based image compression;color image deblurring with impulsive noise;using an oriented PDE to repair image textures;from inpainting to active contours;entropy controlled Gauss-Markov random measure field models for early vision;global minimization of the active contour model with TV-inpainting and two-phase denoising;trimap segmentation for fast and user-friendly alpha matting;a variational approach for object contour tracking;a gradient descent procedure for variational dynamic surface problems with constraints;regularization of mappings between implicit manifolds of arbitrary dimension and codimension;and lens distortion calibration using level sets.

关键词： computer vision

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AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2

AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection...

引用

ieee workshop on Applications of computer vision (WACV)

作者： Simon Damm Mike Laszkiewicz Johannes Lederer Asja Fischer Department of Computer Science Ruhr University Bochum Germany Department of Mathematics Computer Science and Natural Sciences University of Hamburg Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision-language models. We affirm this by adapting DINOv2 for oneshot and few-shot anomaly detection, with a focus on industrial applications. We show that this approach does not only rival existing techniques but can even outmatch them in many settings. Our proposed vision-only approach, AnomalyDINO, follows the well-established patchlevel deep nearest neighbor paradigm, and enables both image-level anomaly prediction and pixel-level anomaly segmentation. The approach is methodologically simple and training-free and, thus, does not require any additional data for fine-tuning or meta-learning. Despite its simplicity, AnomalyDINO achieves state-of-the-art results in oneand few-shot anomaly detection (e.g., pushing the one-shot performance on MVTec-AD from an AUROC of 93.1% to 96.6%). The reduced overhead, coupled with its outstanding few-shot performance, makes AnomalyDINO a strong candidate for fast deployment, e.g., in industrial contexts.

关键词： Metalearning Location awareness Visualization Image segmentation Smoothing methods Foundation models Pipelines Throughput Anomaly detection Standards

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PivotAlign: Improve Semi-Supervised Learning by Learning Intra-Class Heterogeneity and Aligning with Pivots

PivotAlign: Improve Semi-Supervised Learning by Learning Int...

引用

ieee workshop on Applications of computer vision (WACV)

作者： Lingjie Yi Tao Sun Yikai Zhang Songzhu Zheng Weimin Lyu Haibin Ling Chao Chen Stony Brook University Morgan Stanley

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Self-supervised learning plays an important role in current state-of-the-art semi-supervised learning (SSL) methods. These methods learn inter-class heterogeneity among data and generate pseudo-labels based on class level representations. However, they often neglect intra-class heterogeneity, resulting in the under-exploitation of finer-grained semantic relationships within classes. To address this limitation, we introduce PivotAlign, a novel SSL approach that aims to 1) learn hierarchical representations to detect both interclass and intra-class semantic relationships, and 2) refine pseudo-labels based on learned representations with a class-debiasing strategy. Specifically, we first learn a set of pivots as sub-prototypes of classes. We then train representations so that features align with the assigned pivot and are hierarchically grouped based on both inter-class and intra-class heterogeneity. This allows us to capture both inter-class and intra-class semantic relationships among data and leverage them to better assign and refine pseudo-labels. Additionally, since SSL methods are prone to bias toward classes that are easier to learn, we further re-balance class predictions to alleviate this class bias. We demonstrate the effectiveness of PivotAlign on various SSL benchmarks, where PivotAlign achieves state-of-the-art performances. The source code will be released upon publication of the work.

关键词： computer vision Source coding Computational modeling Semantics Self-supervised learning Semisupervised learning Benchmark testing

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