作者: 招人嫉妒 時間: 2025-3-21 23:33
SegBlocks: Towards Block-Based Adaptive Resolution Networks for Fast Segmentation Our method dynamically splits the image into blocks and processes low-complexity regions at a lower resolution. Our novel BlockPad module, implemented in CUDA, replaces zero-padding in order to prevent the discontinuities at patch borders of which existing methods suffer, while keeping memory consu作者: Paradox 時間: 2025-3-22 01:08
Weight-Dependent Gates for Differentiable Neural Network PruningWe argue that the pruning decision should depend on the convolutional weights, in other words, it should be a learnable function of filter weights. We thus construct the weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary filter gates to prune or keep the 作者: 草率男 時間: 2025-3-22 05:58 作者: 鴕鳥 時間: 2025-3-22 10:06
An Efficient Method for Face Quality Assessment on the Edgefirst step generates multiple detections for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detections of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just app作者: 本土 時間: 2025-3-22 16:02
Efficient Approximation of Filters for High-Accuracy Binary Convolutional Neural Networksfull-precision convolutional filter by sum of binary filters with multiplicative and additive scaling factors. We present closed form solutions to the proposed methods. We perform experiments on binary neural networks with binary activations and pre-trained neural networks with full-precision activa作者: 本土 時間: 2025-3-22 19:43
One Weight Bitwidth to Rule Them Allially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed作者: 綠州 時間: 2025-3-23 00:11 作者: 全國性 時間: 2025-3-23 04:19
Post Training Mixed-Precision Quantization Based on Key Layers Selectionsimple to use, they have gained considerable attention. However, when the model is quantized below 8-bits, significant accuracy degradation will be involved. This paper seeks to address this problem by building mixed-precision inference networks based on key activation layers selection. In post trai作者: extinct 時間: 2025-3-23 05:53
Subtensor Quantization for Mobilenets not all DNN designs are friendly to quantization. For example, the popular Mobilenet architecture has been tuned to reduce parameter size and computational latency with separable depthwise convolutions, but not all quantization algorithms work well and the accuracy can suffer against its float poin作者: COWER 時間: 2025-3-23 09:50 作者: 污點 時間: 2025-3-23 15:05 作者: overwrought 時間: 2025-3-23 20:02
Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labelslabels) can deteriorate supervised learning performance significantly as it makes models to be trained with wrong targets. There are technics to train models in the presence of noise in data labels, but they usually suffer from the data inefficiency or overhead of additional steps. In this work, we 作者: Infantry 時間: 2025-3-23 23:50 作者: Lineage 時間: 2025-3-24 03:41 作者: Slit-Lamp 時間: 2025-3-24 09:16
Challenges from Fast Camera Motion and Image Blur: Dataset and EvaluationIn our dataset, image sequences with different camera speeds featuring the same scene and the same camera trajectory. To synthesize a photo-realistic image sequence with fast camera motions, we propose an image blur synthesis method that generates blurry images by their sharp images, camera motions 作者: averse 時間: 2025-3-24 12:42
Self-supervised Attribute-Aware Refinement Network for Low-Quality Text Recognitionegular shapes. Training a model for text recognition with such types of degradations is notoriously hard. In this work, we analyze this problem in terms of two attributes: semantic and a geometric attribute, which are crucial cues for describing low-quality text. To handle this issue, we propose a n作者: aerobic 時間: 2025-3-24 15:55
0302-9743 he 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic..The 249 full papers, 18 short papers, and 21 further contributions included in the workshop pro作者: Employee 時間: 2025-3-24 19:31
Nicole Schneeweis,Rudolf Winter-Ebmern algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects.作者: reserve 時間: 2025-3-25 01:13
Reinforcement Learning for Improving Object Detectionn algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects.作者: nitric-oxide 時間: 2025-3-25 06:24
Conference proceedings 2020bedded Vision Workshop; Real-World Computer Vision from Inputs with Limited Quality (RLQ); The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS 2020); The Visual Object Tracking Challenge Workshop (VOT 2020); and Video Turing Test: Toward Human-Level Video Story Understanding.?.作者: 本能 時間: 2025-3-25 09:02 作者: 鋼盔 時間: 2025-3-25 15:17
The Oecd Mediterranean Regional Projecteline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach’s efficiency through comparison with state-of-the-art face quality regression models on different data sets and real-life scenarios.作者: Trabeculoplasty 時間: 2025-3-25 18:57
https://doi.org/10.1007/978-3-319-78506-6rocess. We conducted experiments to demonstrate the effectiveness of the proposed method with public benchmark datasets: CIFAR-10, CIFAR-100 and Tiny-ImageNet. They showed that our method successfully identified correct labels and performed better than other state-of-the-art algorithms for noisy labels.作者: 搜尋 時間: 2025-3-25 23:05
Michael G. Webb,Martin J. Rickettstionally, we determine that anti-aliased models significantly improve local invariance but do not impact global invariance. Finally, we provide a code repository for experiment reproduction, as well as a website to interact with our results at ..作者: Trochlea 時間: 2025-3-26 04:13
Michael G. Webb,Martin J. Rickettsstream methods of two relevant tasks: visual SLAM and image deblurring. Through our evaluations, we draw some conclusions about the robustness of these methods in the face of different camera speeds and image motion blur.作者: 旁觀者 時間: 2025-3-26 06:34 作者: 假設(shè) 時間: 2025-3-26 10:39
An Efficient Method for Face Quality Assessment on the Edgeeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach’s efficiency through comparison with state-of-the-art face quality regression models on different data sets and real-life scenarios.作者: 狗窩 時間: 2025-3-26 16:12
Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labelsrocess. We conducted experiments to demonstrate the effectiveness of the proposed method with public benchmark datasets: CIFAR-10, CIFAR-100 and Tiny-ImageNet. They showed that our method successfully identified correct labels and performed better than other state-of-the-art algorithms for noisy labels.作者: 起皺紋 時間: 2025-3-26 20:18 作者: elastic 時間: 2025-3-26 22:42
Challenges from Fast Camera Motion and Image Blur: Dataset and Evaluationstream methods of two relevant tasks: visual SLAM and image deblurring. Through our evaluations, we draw some conclusions about the robustness of these methods in the face of different camera speeds and image motion blur.作者: 初學(xué)者 時間: 2025-3-27 02:21
Conference proceedings 2020ropean Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic..The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings w作者: 同音 時間: 2025-3-27 08:36
Criteria for Public Expenditure on Education experimental results indicate an impressive promotion with our method. Relative to ResNet-50(W8A8) and VGG-16(W8A8), our proposed method can accelerate inference with lower power consumption and a little accuracy loss.作者: 被詛咒的人 時間: 2025-3-27 09:46
https://doi.org/10.1007/978-1-349-08464-7w objects given only a single demonstration. By first training the SGMs in a meta-learning manner on a set of common objects, during fine-tuning, the SGMs provided the model with accurate gradients to successfully learn to grasp new objects. We have shown that our method has comparable results to using standard backpropagation.作者: Electrolysis 時間: 2025-3-27 17:30
https://doi.org/10.1007/978-3-319-78506-6The idea of TLAT is to interpolate the target labels of adversarial examples with the ground-truth labels. We show that M-TLAT can increase the robustness of image classifiers towards nineteen common corruptions and five adversarial attacks, without reducing the accuracy on clean samples.作者: IVORY 時間: 2025-3-27 20:09
Post Training Mixed-Precision Quantization Based on Key Layers Selection experimental results indicate an impressive promotion with our method. Relative to ResNet-50(W8A8) and VGG-16(W8A8), our proposed method can accelerate inference with lower power consumption and a little accuracy loss.作者: 空洞 時間: 2025-3-27 22:12
Feed-Forward On-Edge Fine-Tuning Using Static Synthetic Gradient Modulesw objects given only a single demonstration. By first training the SGMs in a meta-learning manner on a set of common objects, during fine-tuning, the SGMs provided the model with accurate gradients to successfully learn to grasp new objects. We have shown that our method has comparable results to using standard backpropagation.作者: albuminuria 時間: 2025-3-28 03:31
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial TrainingThe idea of TLAT is to interpolate the target labels of adversarial examples with the ground-truth labels. We show that M-TLAT can increase the robustness of image classifiers towards nineteen common corruptions and five adversarial attacks, without reducing the accuracy on clean samples.作者: 蚊帳 時間: 2025-3-28 06:40
SegBlocks: Towards Block-Based Adaptive Resolution Networks for Fast Segmentationmption under control. We demonstrate SegBlocks on Cityscapes semantic segmentation, where the number of floating point operations is reduced by 30% with only 0.2% loss in accuracy (mIoU), and an inference speedup of 50% is achieved with 0.7% decrease in mIoU.作者: Urgency 時間: 2025-3-28 11:41 作者: 裂隙 時間: 2025-3-28 18:23 作者: ARCHE 時間: 2025-3-28 22:37
The Labour Market for Educators,mption under control. We demonstrate SegBlocks on Cityscapes semantic segmentation, where the number of floating point operations is reduced by 30% with only 0.2% loss in accuracy (mIoU), and an inference speedup of 50% is achieved with 0.7% decrease in mIoU.作者: intercede 時間: 2025-3-29 01:17 作者: 萬神殿 時間: 2025-3-29 05:24 作者: 者變 時間: 2025-3-29 09:23 作者: 先驅(qū) 時間: 2025-3-29 11:33 作者: GNAT 時間: 2025-3-29 16:57 作者: chisel 時間: 2025-3-29 20:39 作者: Maximize 時間: 2025-3-30 00:13 作者: 廢墟 時間: 2025-3-30 05:36 作者: 傾聽 時間: 2025-3-30 09:12 作者: 無力更進(jìn) 時間: 2025-3-30 13:09 作者: 不怕任性 時間: 2025-3-30 20:04
The Oecd Mediterranean Regional Projectfull-precision convolutional filter by sum of binary filters with multiplicative and additive scaling factors. We present closed form solutions to the proposed methods. We perform experiments on binary neural networks with binary activations and pre-trained neural networks with full-precision activa作者: PACT 時間: 2025-3-30 22:36
https://doi.org/10.1007/978-1-349-08464-7ially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed作者: 分離 時間: 2025-3-31 02:07 作者: ARC 時間: 2025-3-31 07:35 作者: accessory 時間: 2025-3-31 10:49 作者: Repatriate 時間: 2025-3-31 14:31 作者: 下垂 時間: 2025-3-31 17:30
