作者: 投票 時(shí)間: 2025-3-21 23:52
Online Ensemble Model Compression Using Knowledge Distillation,ltaneously learnt ensemble knowledge onto each of the compressed student models. Each model learns unique representations from the data distribution due to its distinct architecture. This helps the ensemble generalize better by combining every model’s knowledge. The distilled students and ensemble t作者: 關(guān)心 時(shí)間: 2025-3-22 04:12
Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System,ration. Many techniques for detecting pupil centers with error range of iris radius have been developed, but few techniques have precise performance with error range of pupil radius. In addition, the conventional methods rarely guarantee real-time pupil center detection in a general-purpose computer作者: prolate 時(shí)間: 2025-3-22 05:05
Efficient Residue Number System Based Winograd Convolution,ations represented in floating point. However it is difficult to apply the scheme to the inference of low-precision quantized (e.g. INT8) networks. Our work extends the Winograd algorithm to Residue Number System (RNS). The minimal complexity convolution is computed precisely over large transformati作者: 阻撓 時(shí)間: 2025-3-22 10:23
Robust Tracking Against Adversarial Attacks,p tracking algorithms against adversarial attacks. Current studies on adversarial attack and defense mainly reside in a single image. In this work, we first attempt to generate adversarial examples on top of video sequences to improve the tracking robustness against adversarial attacks. To this end,作者: 天然熱噴泉 時(shí)間: 2025-3-22 13:35 作者: 天然熱噴泉 時(shí)間: 2025-3-22 19:49 作者: irreducible 時(shí)間: 2025-3-22 23:48 作者: 符合你規(guī)定 時(shí)間: 2025-3-23 02:52
Towards Fast, Accurate and Stable 3D Dense Face Alignment,balance among speed, accuracy and stability. Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously. To further improve the stability on videos, we 作者: 使成核 時(shí)間: 2025-3-23 06:28 作者: BOAST 時(shí)間: 2025-3-23 11:21 作者: Obstreperous 時(shí)間: 2025-3-23 14:11
Toward Faster and Simpler Matrix Normalization via Rank-1 Update,ssive performance of bilinear pooling. The standard matrix normalization, however, needs singular value decomposition (SVD), which is not well suited in the GPU platform, limiting its efficiency in training and inference. To resolve this issue, the Newton-Schulz (NS) iteration method has been propos作者: Rheumatologist 時(shí)間: 2025-3-23 18:56
Accurate Polarimetric BRDF for Real Polarization Scene Rendering,roblems of polarization. To overcome such problems, some research works have suggested to use Convolutional Neural Network (CNN). But acquiring large scale dataset with polarization information is a very difficult task. If there is an accurate model which can describe a complicated phenomenon of pol作者: FEMUR 時(shí)間: 2025-3-24 01:33
Lensless Imaging with Focusing Sparse URA Masks in Long-Wave Infrared and Its Application for Humantwo parts: a novel lensless imaging method that utilizes the idea of local directional focusing for optimal binary sparse coding, and lensless imaging simulator based on Fresnel-Kirchhoff diffraction approximation. Our lensless imaging approach, besides being computationally efficient, is calibratio作者: Bother 時(shí)間: 2025-3-24 03:55 作者: biopsy 時(shí)間: 2025-3-24 07:42 作者: 巧辦法 時(shí)間: 2025-3-24 12:30
UFO,: A Unified Framework Towards Omni-supervised Object Detection, cheaper but less expressive image-level tags. However, real-world annotations are often diverse in form, which challenges these existing works. In this paper, we present UFO., a unified object detection framework that can handle different forms of supervision simultaneously. Specifically, UFO. inco作者: Digitalis 時(shí)間: 2025-3-24 15:28
iCaps: An Interpretable Classifier via Disentangled Capsule Networks, last layer is called a class capsule, which is a vector whose norm indicates a predicted probability for the class. Using the class capsule, existing Capsule Networks already provide some level of interpretability. However, there are two limitations which degrade its interpretability: 1) the class 作者: 智力高 時(shí)間: 2025-3-24 19:29 作者: 沉積物 時(shí)間: 2025-3-25 01:22
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: Excise 時(shí)間: 2025-3-25 04:12
Globalization and the Current Crisison tile (e.g. . to .) of filters and activation patches using the Winograd transformation and low cost (e.g. 8-bit) arithmetic without degrading the prediction accuracy of the networks during inference. The arithmetic complexity reduction is up to . while the performance improvement is up to . to . for . and . filters respectively.作者: 咯咯笑 時(shí)間: 2025-3-25 07:54
Public Finances and the Financial Systemethod can switch between artistic and photo-realistic style transfers and reduce distortion and artifacts. Finally, we show it can be used for applications requiring spatial control and multiple-style transfer.作者: Hemiplegia 時(shí)間: 2025-3-25 14:41 作者: 艦旗 時(shí)間: 2025-3-25 18:30 作者: 中世紀(jì) 時(shí)間: 2025-3-25 20:24
Lessons from Statistical Financedels to validate our framework’s effectiveness. Notably, using our framework a 97% compressed ResNet110 student model managed to produce a 10.64% relative accuracy gain over its individual baseline training on CIFAR100 dataset. Similarly a 95% compressed DenseNet-BC (k?=?12) model managed a 8.17% relative accuracy gain.作者: 巧辦法 時(shí)間: 2025-3-26 00:40 作者: 身體萌芽 時(shí)間: 2025-3-26 05:26
Online Ensemble Model Compression Using Knowledge Distillation,dels to validate our framework’s effectiveness. Notably, using our framework a 97% compressed ResNet110 student model managed to produce a 10.64% relative accuracy gain over its individual baseline training on CIFAR100 dataset. Similarly a 95% compressed DenseNet-BC (k?=?12) model managed a 8.17% relative accuracy gain.作者: affluent 時(shí)間: 2025-3-26 08:27 作者: 講個(gè)故事逗他 時(shí)間: 2025-3-26 13:26
Efficient Residue Number System Based Winograd Convolution,on tile (e.g. . to .) of filters and activation patches using the Winograd transformation and low cost (e.g. 8-bit) arithmetic without degrading the prediction accuracy of the networks during inference. The arithmetic complexity reduction is up to . while the performance improvement is up to . to . for . and . filters respectively.作者: ELUC 時(shí)間: 2025-3-26 19:16
Iterative Feature Transformation for Fast and Versatile Universal Style Transfer,ethod can switch between artistic and photo-realistic style transfers and reduce distortion and artifacts. Finally, we show it can be used for applications requiring spatial control and multiple-style transfer.作者: 苦惱 時(shí)間: 2025-3-27 00:49
https://doi.org/10.1007/978-3-322-94763-5h reconstruction and relighting. We demonstrate in extensive qualitative and quantitative experiments that our network generalizes very well to real images, achieving high-quality shape and material estimation, as well as image-based relighting. Code, models and data will be publicly released.作者: FADE 時(shí)間: 2025-3-27 04:09
The Economic Development of Chinaise of high accuracy and stability, our model runs at 50?fps on a single CPU core and outperforms other state-of-the-art heavy models simultaneously. Experiments on several challenging datasets validate the efficiency of our method. The code and models will be available at ..作者: 使成波狀 時(shí)間: 2025-3-27 09:17 作者: 惡心 時(shí)間: 2025-3-27 10:22 作者: LAP 時(shí)間: 2025-3-27 14:15
Capital Formation and its Sourcesmental learning phases. Comprehensive experiments on CIFAR100, ImageNet, and subImageNet datasets demonstrate the power of the TPCIL for continuously learning new classes with less forgetting. The code will be released.作者: 疲憊的老馬 時(shí)間: 2025-3-27 19:45
Labour Market and Dual Structuretwo limitations using a novel class-supervised disentanglement algorithm and an additional regularizer, respectively. Through quantitative and qualitative evaluations on three datasets, we demonstrate that the resulting classifier, ., provides a prediction along with clear rationales behind it with no performance degradation.作者: 遺忘 時(shí)間: 2025-3-27 22:22
Single-Shot Neural Relighting and SVBRDF Estimation,h reconstruction and relighting. We demonstrate in extensive qualitative and quantitative experiments that our network generalizes very well to real images, achieving high-quality shape and material estimation, as well as image-based relighting. Code, models and data will be publicly released.作者: 者變 時(shí)間: 2025-3-28 04:58 作者: 吞沒(méi) 時(shí)間: 2025-3-28 06:18
Accurate Polarimetric BRDF for Real Polarization Scene Rendering,BRDF) model. We prove its accuracy by fitting our model to measured data with variety of light and camera conditions. We render polarized images using this model and use them to estimate surface normal. Experiments show that the CNN trained by our polarized images has more accuracy than one trained by RGB only.作者: 憤慨點(diǎn)吧 時(shí)間: 2025-3-28 11:50
Lensless Imaging with Focusing Sparse URA Masks in Long-Wave Infrared and Its Application for Humanimage generation for CNN training. We demonstrate the advantages of our framework on a dual-camera system (RGB-LWIR lensless), where we perform CNN-based human detection using the fused RGB-LWIR data.作者: Banister 時(shí)間: 2025-3-28 18:34
Topology-Preserving Class-Incremental Learning,mental learning phases. Comprehensive experiments on CIFAR100, ImageNet, and subImageNet datasets demonstrate the power of the TPCIL for continuously learning new classes with less forgetting. The code will be released.作者: 匯總 時(shí)間: 2025-3-28 22:45 作者: impale 時(shí)間: 2025-3-29 01:39
https://doi.org/10.1007/978-3-030-58529-7Computer Science; Informatics; Conference Proceedings; Research; Applications作者: 表狀態(tài) 時(shí)間: 2025-3-29 06:12
978-3-030-58528-0Springer Nature Switzerland AG 2020作者: 百科全書(shū) 時(shí)間: 2025-3-29 08:47
https://doi.org/10.1057/9780230105690 iterative inpainting method with a feedback mechanism. Specifically, we introduce a deep generative model which not only outputs an inpainting result but also a corresponding confidence map. Using this map as feedback, it progressively fills the hole by trusting only high-confidence pixels inside t作者: 無(wú)法取消 時(shí)間: 2025-3-29 12:07
Lessons from Statistical Financeltaneously learnt ensemble knowledge onto each of the compressed student models. Each model learns unique representations from the data distribution due to its distinct architecture. This helps the ensemble generalize better by combining every model’s knowledge. The distilled students and ensemble t作者: 土產(chǎn) 時(shí)間: 2025-3-29 18:59
Lessons from Statistical Financeration. Many techniques for detecting pupil centers with error range of iris radius have been developed, but few techniques have precise performance with error range of pupil radius. In addition, the conventional methods rarely guarantee real-time pupil center detection in a general-purpose computer作者: DIS 時(shí)間: 2025-3-29 20:39 作者: mosque 時(shí)間: 2025-3-30 03:42
The Process Model and Loss Function,p tracking algorithms against adversarial attacks. Current studies on adversarial attack and defense mainly reside in a single image. In this work, we first attempt to generate adversarial examples on top of video sequences to improve the tracking robustness against adversarial attacks. To this end,作者: 值得贊賞 時(shí)間: 2025-3-30 05:25
https://doi.org/10.1007/978-3-322-94763-5s, using a single image captured by a mobile phone camera. Our physically-based modeling leverages a deep cascaded architecture trained on a large-scale synthetic dataset that consists of complex shapes with microfacet SVBRDF. In contrast to prior works that train rendering layers subsequent to inve作者: 頑固 時(shí)間: 2025-3-30 11:19 作者: 影響深遠(yuǎn) 時(shí)間: 2025-3-30 15:43
Economic Systems and the Government, improvements in recognition quality. However, it performs poorly on contextless texts (., random character sequences) which is unacceptable in most of real application scenarios. In this paper, we first deeply investigate the decoding process of the decoder. We empirically find that a representativ作者: Canary 時(shí)間: 2025-3-30 20:29 作者: 放牧 時(shí)間: 2025-3-30 22:10
Public Finances and the Financial Systemansformation that iteratively stylizes features with analytical gradient descent (Implementation is open-sourced at .). Experiments show this transformation is advantageous in part because it is fast. With control knobs to balance content preservation and style effect transferal, we also show this m作者: 骯臟 時(shí)間: 2025-3-31 02:28
Agriculture during Industrializationarticular search spaces. They also lack efficient mechanisms to reuse knowledge when confronting multiple tasks. These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. 作者: PANT 時(shí)間: 2025-3-31 08:14 作者: Cognizance 時(shí)間: 2025-3-31 12:02
Public Finances and the Financial Systemroblems of polarization. To overcome such problems, some research works have suggested to use Convolutional Neural Network (CNN). But acquiring large scale dataset with polarization information is a very difficult task. If there is an accurate model which can describe a complicated phenomenon of pol作者: Disk199 時(shí)間: 2025-3-31 15:23
Public Finances and the Financial Systemtwo parts: a novel lensless imaging method that utilizes the idea of local directional focusing for optimal binary sparse coding, and lensless imaging simulator based on Fresnel-Kirchhoff diffraction approximation. Our lensless imaging approach, besides being computationally efficient, is calibratio作者: 突變 時(shí)間: 2025-3-31 21:36
Capital Formation and its Sourcesen incrementally learning new classes. To alleviate forgetting, we put forward to preserve the old class knowledge by maintaining the topology of the network’s feature space. On this basis, we propose a novel . (TPCIL) framework. TPCIL uses an . (EHG) to model the feature space topology, which is co作者: 在駕駛 時(shí)間: 2025-3-31 22:46
Capital Formation and Its Sourcesssification networks are often not accurate due to the lack of fine pixel-level supervision. In this paper, we propose to leverage pixel-level similarities across different objects for learning more accurate object locations in a complementary way. Particularly, two kinds of constraints are proposed作者: Osteons 時(shí)間: 2025-4-1 04:41 作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-4-1 09:04
