作者: 未開化 時間: 2025-3-21 21:46 作者: 征稅 時間: 2025-3-22 02:42
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234282.jpg作者: 吼叫 時間: 2025-3-22 06:39
HLA and ABO antigens in keratoconus patientsesources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficient general purpose networks due to their usefulness in several application areas. In this work, we strive to effectively combine the strengths of both CNN and Transformer models and propose a 作者: 黑豹 時間: 2025-3-22 11:17
HLA and ABO antigens in keratoconus patientsference. This paper offers a comprehensive introduction and guide to CINs and their implementation, as well as best-practices and code examples for composing basic modules into complex neural network architectures that perform online inference with an order of magnitude less floating-point operation作者: 河流 時間: 2025-3-22 16:44
HLA and ABO antigens in keratoconus patientshis is that self-attention scales quadratically with the number of tokens, which in turn, scales quadratically with the image size. On larger images (e.g., 1080p), over 60% of the total computation in the network is spent solely on creating and applying attention matrices. We take a step toward solv作者: 河流 時間: 2025-3-22 18:14
Studies in Computational Intelligencefor reduced memory usage and computation, while preserving the performance of the original model. However, extreme quantization (1-bit weight/1-bit activations) of compactly-designed backbone architectures (e.g., MobileNets) often used for edge-device deployments results in severe performance degene作者: Cacophonous 時間: 2025-3-22 23:11 作者: 連累 時間: 2025-3-23 04:08
Studies in Computational Intelligenceover time. Dynamic network architectures are one of the existing techniques developed to handle the varying computational load in real-time deployments. Here we introduce LeAF (Legacy Augmentation for Flexible inference), a novel paradigm to augment the key-phases of a pre-trained DNN with alternati作者: crutch 時間: 2025-3-23 08:22 作者: committed 時間: 2025-3-23 13:22
https://doi.org/10.1007/978-3-8349-9632-9 demand. Many of the methods emphasize optimization of a specific per-layer . (DoF), such as grid step size, preconditioning factors, nudges to weights and biases, often chained to others in multi-step solutions. Here we rethink quantized network parameterization in HW-aware fashion, towards a unifi作者: 慷慨援助 時間: 2025-3-23 15:31 作者: vasospasm 時間: 2025-3-23 19:04 作者: ABOUT 時間: 2025-3-23 22:13 作者: Juvenile 時間: 2025-3-24 03:16 作者: cunning 時間: 2025-3-24 10:13
https://doi.org/10.1007/978-3-8349-9632-9dget costs, inefficient productivity, and poor performance. This paper addresses the challenge of high-accuracy primitive instance segmentation from point clouds with the support of IFC model as a core stage to facilitate maintaining a geometric digital twin during the construction stage. Keeping th作者: 不愛防注射 時間: 2025-3-24 13:42
Research in Management Accounting & Controlor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt. Human annotations for material categories on sensor-generated data are scarce and cost-intensive. Currently, representation learn作者: Lipoprotein 時間: 2025-3-24 18:48 作者: botany 時間: 2025-3-24 19:57
Computer Vision – ECCV 2022 Workshops978-3-031-25082-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 獸皮 時間: 2025-3-25 00:08
https://doi.org/10.1007/978-3-031-22573-4ity of reconstructed images. We propose novel channel pruning and knowledge distillation techniques that are specialized for image inpainting models with mask information. Experimental results demonstrate that our compressed inpainting model with only one-tenth of the model size achieves similar performance to the full model.作者: Encephalitis 時間: 2025-3-25 03:23
Deep Neural Network Compression for?Image Inpaintingity of reconstructed images. We propose novel channel pruning and knowledge distillation techniques that are specialized for image inpainting models with mask information. Experimental results demonstrate that our compressed inpainting model with only one-tenth of the model size achieves similar performance to the full model.作者: HAUNT 時間: 2025-3-25 07:47 作者: 起波瀾 時間: 2025-3-25 13:49
HLA and ABO antigens in keratoconus patientsmposing basic modules into complex neural network architectures that perform online inference with an order of magnitude less floating-point operations than their non-CIN counterparts. Continual Inference provides drop-in replacements of PyTorch modules and is readily downloadable via the Python Package Index and at ..作者: 咆哮 時間: 2025-3-25 17:11 作者: 起皺紋 時間: 2025-3-25 22:32
https://doi.org/10.1007/978-3-8349-9632-9ed analysis of all quantization DoF, permitting for the first time their joint end-to-end finetuning. Our single-step simple and extendable method, dubbed quantization-aware finetuning (QFT), achieves 4b-weights quantization results on-par with SoTA within PTQ constraints of speed and resource.作者: Vasodilation 時間: 2025-3-26 02:40 作者: 提升 時間: 2025-3-26 04:53
QFT: Post-training Quantization via?Fast Joint Finetuning of?All Degrees of?Freedomed analysis of all quantization DoF, permitting for the first time their joint end-to-end finetuning. Our single-step simple and extendable method, dubbed quantization-aware finetuning (QFT), achieves 4b-weights quantization results on-par with SoTA within PTQ constraints of speed and resource.作者: 辭職 時間: 2025-3-26 09:06
HLA and ABO antigens in keratoconus patientsinear in both tokens and features with no hidden constants, making it significantly faster than standard self-attention in an off-the-shelf ViT-B/16 by a factor of the token count. Moreover, Hydra Attention retains high accuracy on ImageNet and, in some cases, actually . it.作者: 印第安人 時間: 2025-3-26 12:40
Studies in Computational Intelligenceand can also be used during training to achieve improved performance. Unlike previous methods, PANN incurs only a minor degradation in accuracy w.r.t.?the full-precision version of the network and enables to seamlessly traverse the power-accuracy trade-off at deployment time.作者: 滔滔不絕地講 時間: 2025-3-26 19:09
Research in Management Accounting & Controlthat a combination of weight and activation pruning is superior to each option separately. Furthermore, during the training, the choice between pruning the weights of activations can be motivated by practical inference costs (e.g., memory bandwidth). We demonstrate the efficiency of the approach on several image classification datasets.作者: crumble 時間: 2025-3-26 23:09 作者: originality 時間: 2025-3-27 01:56 作者: Ibd810 時間: 2025-3-27 08:15 作者: Vulnerable 時間: 2025-3-27 11:46
Hydra Attention: Efficient Attention with?Many Headsinear in both tokens and features with no hidden constants, making it significantly faster than standard self-attention in an off-the-shelf ViT-B/16 by a factor of the token count. Moreover, Hydra Attention retains high accuracy on ImageNet and, in some cases, actually . it.作者: 記憶 時間: 2025-3-27 14:08
Power Awareness in?Low Precision Neural Networksand can also be used during training to achieve improved performance. Unlike previous methods, PANN incurs only a minor degradation in accuracy w.r.t.?the full-precision version of the network and enables to seamlessly traverse the power-accuracy trade-off at deployment time.作者: BUST 時間: 2025-3-27 21:39 作者: FER 時間: 2025-3-27 23:11
Image Illumination Enhancement for?Construction Worker Pose Estimation in?Low-light Conditions worker pose estimation. In addition, the proposed UIRE-Net restores image brightness without relying on image pairs. A testing experiment based on nighttime construction workers is conducted to validate the veracity.作者: MANIA 時間: 2025-3-28 04:10 作者: 觀點 時間: 2025-3-28 08:20
PriSeg: IFC-Supported Primitive Instance Geometry Segmentation with?Unsupervised Clusteringe descriptor and unsupervised clustering algorithm. The proposed solution is robust in real complex environments, such as point clouds are noisy with high occlusions and clutter, the as-built status deviates from the as-designed model in terms of position, orientation, and scale.作者: Kernel 時間: 2025-3-28 13:30
0302-9743 xt in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for 978-3-031-25081-1978-3-031-25082-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: ostensible 時間: 2025-3-28 15:33
HLA and ABO antigens in keratoconus patients of the proposed approach, outperforming state-of-the-art methods with comparatively lower compute requirements. Our EdgeNeXt model with 1.3M parameters achieves 71.2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2.2% with 28% reduction in FLOPs. Further, our EdgeN作者: MEAN 時間: 2025-3-28 22:31 作者: 合唱團 時間: 2025-3-29 02:31
Studies in Computational Intelligenceth non-legacy and less flexible methods. We examine how LeAF’s dynamic routing strategy impacts the accuracy and the use of the available computational resources as a function of the compute capability and load of the device, with particular attention to the case of an unpredictable batch size. We s作者: 摸索 時間: 2025-3-29 05:25 作者: Jejune 時間: 2025-3-29 08:36
Research in Management Accounting & Controlhieves an F1 score of 0.73. Further, The proposed method yields an F1 score of 0.65 with an 11% improvement over ImageNet transfer learning performance in a semi-supervised setting when only 20% of labels are used in fine-tuning. Finally, the Proposed method showcases improved performance generaliza作者: 腫塊 時間: 2025-3-29 12:55 作者: 英寸 時間: 2025-3-29 17:27
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for?Mobile Vision Applications of the proposed approach, outperforming state-of-the-art methods with comparatively lower compute requirements. Our EdgeNeXt model with 1.3M parameters achieves 71.2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2.2% with 28% reduction in FLOPs. Further, our EdgeN作者: GOAD 時間: 2025-3-29 19:54
BiTAT: Neural Network Binarization with?Task-Dependent Aggregated Transformationion matrix and importance vector, such that each weight is disentangled from the others. Then, we quantize the weights based on their importance to minimize the loss of the information from the original weights/activations. We further perform progressive layer-wise quantization from the bottom layer作者: meretricious 時間: 2025-3-30 00:36
Augmenting Legacy Networks for?Flexible Inferenceth non-legacy and less flexible methods. We examine how LeAF’s dynamic routing strategy impacts the accuracy and the use of the available computational resources as a function of the compute capability and load of the device, with particular attention to the case of an unpredictable batch size. We s作者: subacute 時間: 2025-3-30 07:15
Towards an?Error-free Deep Occupancy Detector for?Smart Camera Parking Systemo traditional classification solutions. We also introduce an additional SNU-SPS dataset, in which we estimate the system performance from various views and conduct system evaluation in parking assignment tasks. The result from our dataset shows that our system is promising for real-world application作者: 褪色 時間: 2025-3-30 11:55 作者: Hallmark 時間: 2025-3-30 13:44 作者: bypass 時間: 2025-3-30 17:40
Conference proceedings 2023ng for Next-Generation Industry-LevelAutonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for 作者: 盲信者 時間: 2025-3-30 22:51
Facilitating Construction Scene Understanding Knowledge Sharing and?Reuse via?Lifelong Site Object D作者: 教義 時間: 2025-3-31 01:58
A Hyperspectral and?RGB Dataset for?Building Fa?ade Segmentation作者: 染色體 時間: 2025-3-31 06:22
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for?Mobile Vision Applicationsesources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficient general purpose networks due to their usefulness in several application areas. In this work, we strive to effectively combine the strengths of both CNN and Transformer models and propose a 作者: 不成比例 時間: 2025-3-31 13:07 作者: CROAK 時間: 2025-3-31 17:02
Hydra Attention: Efficient Attention with?Many Headshis is that self-attention scales quadratically with the number of tokens, which in turn, scales quadratically with the image size. On larger images (e.g., 1080p), over 60% of the total computation in the network is spent solely on creating and applying attention matrices. We take a step toward solv作者: 有權(quán) 時間: 2025-3-31 17:45 作者: Talkative 時間: 2025-3-31 22:44
Power Awareness in?Low Precision Neural Networksve quantization of weights and activations. However, these methods do not consider the precise power consumed by each module in the network and are therefore not optimal. In this paper we develop accurate power consumption models for all arithmetic operations in the DNN, under various working condit作者: Inflamed 時間: 2025-4-1 03:26
Augmenting Legacy Networks for?Flexible Inferenceover time. Dynamic network architectures are one of the existing techniques developed to handle the varying computational load in real-time deployments. Here we introduce LeAF (Legacy Augmentation for Flexible inference), a novel paradigm to augment the key-phases of a pre-trained DNN with alternati
