找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

[復(fù)制鏈接]
樓主: concord
31#
發(fā)表于 2025-3-26 22:29:44 | 只看該作者
0302-9743 art VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .978-3-031-26312-5978-3-031-26313-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
32#
發(fā)表于 2025-3-27 01:40:53 | 只看該作者
33#
發(fā)表于 2025-3-27 07:58:59 | 只看該作者
34#
發(fā)表于 2025-3-27 09:46:29 | 只看該作者
Dirk Slama,Tanja Rückert,Heiner LasiSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.
35#
發(fā)表于 2025-3-27 16:23:36 | 只看該作者
Multi-Branch Network with?Ensemble Learning for?Text Removal in?the?Wild a patch attention module to perceive text location and generate text attention features. Our method outperforms state-of-the-art approaches on both real-world and synthetic datasets, improving PSNR by 1.78 dB in the SCUT-EnsText dataset and 4.45 dB in the SCUT-Syn dataset.
36#
發(fā)表于 2025-3-27 18:04:06 | 只看該作者
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruningtitative and qualitative experiments with in-depth analyses. Furthermore, we demonstrate the versatility of the proposed distillation-based channel pruning method by applying it to semantic segmentation.
37#
發(fā)表于 2025-3-27 22:57:46 | 只看該作者
Self-Supervised Dehazing Network Using Physical PriorsSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.
38#
發(fā)表于 2025-3-28 02:44:13 | 只看該作者
Conference proceedings 2023ing, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .
39#
發(fā)表于 2025-3-28 09:07:21 | 只看該作者
40#
發(fā)表于 2025-3-28 13:57:14 | 只看該作者
0302-9743 China, December 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applic
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 06:40
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
来凤县| 绵竹市| 邵武市| 大邑县| 扎兰屯市| 迁安市| 宣化县| 珲春市| 闽清县| 青冈县| 东乌珠穆沁旗| 锡林郭勒盟| 潮安县| 东源县| 安阳市| 奎屯市| 汽车| 乐亭县| 宜章县| 武鸣县| 宣化县| 阿坝县| 辽源市| 天水市| 潜江市| 无极县| 凤凰县| 丰台区| 宜宾市| 紫阳县| 蚌埠市| 盈江县| 绥滨县| 资源县| 察哈| 永丰县| 甘肃省| 明星| 体育| 北海市| 鄂尔多斯市|