找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

[复制链接]
楼主: mobility
发表于 2025-3-23 11:42:53 | 显示全部楼层
发表于 2025-3-23 15:14:16 | 显示全部楼层
发表于 2025-3-23 21:58:53 | 显示全部楼层
Henry George on Free Trade and Protection,sing a novel fusion scheme that incorporates channel-wise weights and spatial weighted average. To improve efficiency, we introduce a pre-filtering step that can exclude uninformative saliency maps to improve efficiency while still enhancing overall results. We evaluate SESS on object recognition an
发表于 2025-3-24 00:55:26 | 显示全部楼层
发表于 2025-3-24 02:53:31 | 显示全部楼层
,Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks,tion technique. Furthermore, based on the training-inference-decoupled idea, a series of different forms for implementing . on different levels are presented, which also enjoy shared convolutions in the inference and are friendly to both neuromorphic and non-neuromorphic hardware platforms. A theore
发表于 2025-3-24 10:24:08 | 显示全部楼层
发表于 2025-3-24 12:07:49 | 显示全部楼层
LANA: Latency Aware Network Acceleration,echniques. We analyze three popular network architectures: EfficientNetV1, EfficientNetV2 and ResNeST, and achieve accuracy improvement (up to .) for all models when compressing larger models. LANA achieves significant speed-ups (up to 5.) with minor to no accuracy drop on GPU and CPU. Project page:
发表于 2025-3-24 18:23:33 | 显示全部楼层
发表于 2025-3-24 22:05:57 | 显示全部楼层
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search,hieve . speedup for DNN inference compared to prior hardware-aware NAS methods while attaining similar or improved accuracy in image classification on CIFAR-10 and Imagenet-100 datasets. (Source code is available at .).
发表于 2025-3-25 00:36:11 | 显示全部楼层
,PTQ4ViT: Post-training Quantization for Vision Transformers with Twin Uniform Quantization,sian guided metric to evaluate different scaling factors, which improves the accuracy of calibration at a small cost. To enable the fast quantization of vision transformers, we develop an efficient framework, PTQ4ViT. Experiments show the quantized vision transformers achieve near-lossless predictio
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-5 13:34
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表