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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11897 条记录，以下是1641-1650 订阅

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CellTypeGraph: A New Geometric computer vision Benchmark

CellTypeGraph: A New Geometric Computer Vision Benchmark

引用

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

作者： Cerrone, Lorenzo Vijayan, Athul Mody, Tejasvinee Schneitz, Kay Hamprecht, Fred A. Heidelberg Univ HCI Heidelberg Germany Tech Univ Munich Sch Life Munich Germany Max Planck Inst Plant Breeding Res Cologne Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.

关键词： computer vision Three-dimensional displays Plants (biology) Supervised learning Biological systems computer architecture Benchmark testing

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ENIGMA-51: Towards a Fine-Grained Understanding of Human Behavior in Industrial Scenarios

ENIGMA-51: Towards a Fine-Grained Understanding of Human Beh...

引用

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

作者： Ragusa, Francesco Leonardi, Rosario Mazzamuto, Michele Bonanno, Claudia Scavo, Rosario Furnari, Antonino Farinella, Giovanni Maria Univ Catania FPV IPLab Catania Italy Univ Catania Next Vis Srl Spinoff Catania Italy

ISBN: (纸本)9798350318920;9798350318937

ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotated with a rich set of labels that enable the systematic study of human behavior in the industrial domain. We provide benchmarks on four tasks related to human behavior: 1) untrimmed temporal detection of human-object interactions, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human behavior in industrial scenarios. We publicly release the dataset at https://***/ENIGMA-51.

关键词： Algorithms Algorithms Datasets and evaluations Video recognition and understanding

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Single-Image Deblurring, Trajectory and Shape Recovery of Fast Moving Objects with Denoising Diffusion Probabilistic Models

Single-Image Deblurring, Trajectory and Shape Recovery of Fa...

引用

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

作者： Spetlik, Radim Rozumnyi, Denys Matas, Jiri Czech Tech Univ Fac Elect Engn Prague Czech Republic Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9798350318920;9798350318937

Blurry appearance of fast moving objects in video frames was successfully used to reconstruct the object appearance and motion in both 2D and 3D domains. The proposed method addresses the novel, severely ill-posed, task of single-image fast moving object deblurring, shape, and trajectory recovery - previous approaches require at least three consecutive video frames. Given a single image, the method outputs the object 2D appearance and position in a series of sub-frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed SI-DDPM-FMO method is trained end-to-end on a synthetic dataset with various moving objects, yet it generalizes well to real-world data from several publicly available datasets. SI-DDPM-FMO performs similarly to or better than recent multi-frame methods and a carefully designed baseline method.

关键词： Algorithms Algorithms Low-level and physics-based vision Video recognition and understanding

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ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds

ZZ-Net: A Universal Rotation Equivariant Architecture for 2D...

引用

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

作者： Bokman, Georg Kahla, Fredrik Flinth, Axel Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Umea Univ Dept Math & Math Stat Umea Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision.

关键词： Point cloud compression Deep learning computer vision Neural networks Memory management Estimation computer architecture

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Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis

Neural Textured Deformable Meshes for Robust Analysis-by-Syn...

引用

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

作者： Wang, Angtian Ma, Wufei Yuille, Alan Kortylewski, Adam Johns Hopkins Univ Baltimore MD 21218 USA Univ Freiburg Freiburg Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9798350318920;9798350318937

Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios. It has been conjectured such robustness benefits from performing analysis-by-synthesis. Our paper formulates triple vision tasks in a consistent manner using approximate analysis-by-synthesis by render-and-compare algorithms on neural features. In this work, we introduce Neural Textured Deformable Meshes (NTDM), which involve the object model with deformable geometry that allows optimization on both camera parameters and object geometries. The deformable mesh is parameterized as a neural field, and covered by whole-surface neural texture maps, which are trained to have spatial discriminability. During inference, we extract the feature map of the test image and subsequently optimize the 3D pose and shape parameters of our model using differentiable rendering to best reconstruct the target feature map. We show that our analysis-by-synthesis is much more robust than conventional neural networks when evaluated on real-world images and even in challenging out-of-distribution scenarios, such as occlusion and domain shift. Our algorithms are competitive with standard algorithms when tested on conventional performance measures.

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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HELA-VFA: A Hellinger Distance-Attention-based Feature Aggregation Network for Few-Shot Classification

HELA-VFA: A Hellinger Distance-Attention-based Feature Aggre...

引用

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

作者： Lee, Gao Yu Dam, Tanmoy Poenar, Daniel Puiu Duong, Vu N. Ferdaus, Md Meftahul Nanyang Technol Univ NTU Singapore Singapore Univ New Orleans New Orleans LA USA

ISBN: (纸本)9798350318920;9798350318937

Enabling effective learning using only a few presented examples is a crucial but difficult computer vision objective. Few-shot learning have been proposed to address the challenges, and more recently variational inference-based approaches are incorporated to enhance few-shot classification performances. However, the current dominant strategy utilized the Kullback-Leibler (KL) divergences to find the log marginal likelihood of the target class distribution, while neglecting the possibility of other probabilistic comparative measures, as well as the possibility of incorporating attention in the feature extraction stages, which can increase the effectiveness of the few-shot model. To this end, we proposed the HELlinger-Attention Variational Feature Aggregation network (HELA-VFA), which utilized the Hellinger distance along with attention in the encoder to fulfill the aforementioned gaps. We show that our approach enables the derivation of an alternate form of the lower bound commonly presented in prior works, thus making the variational optimization feasible and be trained on the same footing in a given setting. Extensive experiments performed on four benchmarked few-shot classification datasets demonstrated the feasibility and superiority of our approach relative to the State-Of-The-Arts (SOTAs) approaches.

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

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Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery

Spectral Transfer Guided Active Domain Adaptation For Therma...

引用

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

作者： Ustun, Berkcan Kaya, Ahmet Kagan Ayerden, Ezgi Cakir Altinel, Fazil Aselsan Inc Res Ctr Yenimahalle Turkiye Middle East Tech Univ Dept Elect & Elect Engn Ankara Turkiye

ISBN: (纸本)9798350302493

The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available.(1)

关键词： computer vision

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Improving Normalization with the James-Stein Estimator

Improving Normalization with the James-Stein Estimator

引用

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

作者： Khoshsirat, Seyedalireza Kambhamettu, Chandra Univ Delaware Video Image Modeling & Synth VIMS Lab Newark DE 19716 USA

ISBN: (纸本)9798350318920;9798350318937

Stein's paradox holds considerable sway in high-dimensional statistics, highlighting that the sample mean, traditionally considered the de facto estimator, might not be the most efficacious in higher dimensions. To address this, the James-Stein estimator proposes an enhancement by steering the sample means toward a more centralized mean vector. In this paper, first, we establish that normalization layers in deep learning use inadmissible estimators for mean and variance. Next, we introduce a novel method to employ the James-Stein estimator to improve the estimation of mean and variance within normalization layers. We evaluate our method on different computer vision tasks: image classification, semantic segmentation, and 3D object classification. Through these evaluations, it is evident that our improved normalization layers consistently yield superior accuracy across all tasks without extra computational burden. Moreover, recognizing that a plethora of shrinkage estimators surpass the traditional estimator in performance, we study two other prominent shrinkage estimators: Ridge and LASSO. Additionally, we provide visual representations to intuitively demonstrate the impact of shrinkage on the estimated layer statistics. Finally, we study the effect of regularization and batch size on our modified batch normalization. The studies show that our method is less sensitive to batch size and regularization, improving accuracy under various setups.

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Certified Patch Robustness via Smoothed vision Transformers

Certified Patch Robustness via Smoothed Vision Transformers

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

作者： Salman, Hadi Jain, Saachi Wong, Eric Madry, Aleksander MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.(1)

关键词： Visualization computer vision Smoothing methods Costs Computational modeling computer architecture Transformers

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Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching

Deep orientation-aware functional maps: Tackling symmetry is...

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

作者： Donati, Nicolas Corman, Etienne Ovsjanikov, Maks Ecole Polytech LIX Palaiseau France CNRS INRIA Paris France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

State-of-the-art fully intrinsic network for non-rigid shape matching are unable to disambiguate between shape inner symmetries. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on DiffusionNet, which makes our method robust to discretization changes, while adding a vector-field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.

关键词： Deep learning computer vision Codes Shape computer architecture pattern matching

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