| 5 | 2/1 | 返回列表 |
| 查看: 8608 | 回復(fù): 112 | |||||
| 【有獎(jiǎng)交流】積極回復(fù)本帖子,參與交流,就有機(jī)會(huì)分得作者 sig102657 的 776 個(gè)金幣 ,回帖就立即獲得 2 個(gè)金幣,每人有 1 次機(jī)會(huì) | |||||
| 當(dāng)前只顯示滿足指定條件的回帖,點(diǎn)擊這里查看本話題的所有回帖 | |||||
[交流]
Call for Papers (IEEE Transactions on Neural Networks and Learning Systems Speci
|
|||||
IEEE Transactions on Neural Networks and Learning Systems Call for Papers Special Issue on Effective Feature Fusion in Deep Neural Networks https://cis.ieee.org/images/file ... efdnn_tnnls_cfp.pdf Submission deadline: nov. 30, 2020. first notification: feb. 1, 2021 ================================================================================ Due to the powerful ability of learning hierarchical features, Deep Deural Detworks (DNNs) have achieved great success in many intelligent perception systems with image data and/or point cloud data and have been widely used in developing robust automotive driving, visual surveillance, and human-machine interaction. For example, state-of-the-art performances in image classification, object detection, semantic segmentation, and cross-modal perception are obtained by different kinds of DNNs. To a great degree, the success of DNNs stems from properly fusing the hierarchical features which are diverse in semantic-levels, resolutions/scales, roles, sensitivity, and so on. Representative fusion schemes include dense connection, residual learning, skip connection, top-down feature pyramid, and attention-based feature weighting. However, there is a large room for developing more effective feature fusion to improve the performance of dnns so that machine perception can approach or exceed human perception. This special issue focuses on investigating problems and phenomena of existing feature fusion schemes, tackling the challenges of semantic gap and perception of hard objects and scenarios, and providing new ideas, theories, solutions, and insights for effective feature fusion in DNNs for image and/or point cloud data. The topics of interest include, but are not limited to: n Feature fusion for effective backbones and prediction n Feature fusion for image/video data using deep neural networks n Feature fusion for point cloud data using deep neural networks n Adaptive feature fusion networks n Criteria and loss functions for feature fusion in deep neural networks n Feature fusion for detecting/recognizing small objects n Feature fusion for detecting/recognizing occluded objects n Attention-based feature fusion in deep neural networks n Visualization and interpretation of feature fusion n Feature fusion for semantic segmentation n Feature fusion for object tracking n Feature fusion for cross-modal/domain learning n Feature fusion for 3D object detection n New feature fusion problems and applications IMPORTANT DATAS n November 30, 2020: Deadline for manuscript submission n February 1, 2021: Reviewer’s comments to authors n April 1, 2021: Submission deadline of revisions n June 1, 2021: Final decisions to authors n July 1, 2021: Publication date (Early access) GUEST EDITORS Yanwei Pang, Tianjin University, China, pyw@tju.edu.cn Fahad Shahbaz Khan, Inception Institute of Artificial Intelligence, UAE, fahad.khan@liu.se Xin Lu, Adobe Inc., USA, xinl@adobe.com Fabio Cuzzolin, Oxford Brookes University, UK, fabio.cuzzolin@brookes.ac.uk SUBMISSION INSTRUCTIONS n Read the Information for Authors at https://cis.ieee.org/tnnls. n Submit your manuscript at the TNNLS webpage (https://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Send an email to the leading editor Prof. Yanwei Pang (pyw@tju.edu.cn) with subject “TNNLS special issue submission” to notify your submission. n Early submissions are welcome. We will start the review process as soon as we receive your contributions. |
» 搶金幣啦!回帖就可以得到:
+1/85
+1/85
+1/80
+1/37
+1/37
+1/36
+1/35
+5/35
+1/21
+1/18
+1/16
+1/14
+1/10
+1/5
+1/3
+1/3
+1/2
+1/2
+1/2
+1/2
|
本帖內(nèi)容被屏蔽 |
|
本帖內(nèi)容被屏蔽 |
|
本帖內(nèi)容被屏蔽 |
| 最具人氣熱帖推薦 [查看全部] | 作者 | 回/看 | 最后發(fā)表 | |
|---|---|---|---|---|
|
[基金申請] 面上和青基一樣限30頁不合理 +3 | wowsunflower 2026-03-10 | 5/250 |
|
|---|---|---|---|---|
|
[考研] 一志愿華科071000生物學(xué) 338求調(diào)劑 +4 | 九月九里 2026-03-05 | 4/200 |
|
|
[考研] 0857環(huán)境調(diào)劑 +5 | 熠熠_11 2026-03-10 | 5/250 |
|
|
[考研] 293求調(diào)劑,一志愿陜師大生物學(xué) +3 | ??????.?.??? 2026-03-09 | 3/150 |
|
|
[考研] 考研材料與化工,求調(diào)劑 +7 | 戲精丹丹丹 2026-03-09 | 7/350 |
|
|
[基金申請] 提交后的基金本子,已讓學(xué)校撤回了,可否換口子提交 +3 | dut_pfx 2026-03-10 | 3/150 |
|
|
[考研] 調(diào)劑 +5 | 呵唔哦豁 2026-03-10 | 5/250 |
|
|
[考研] 085602化工求調(diào)劑 +7 | 董boxing 2026-03-10 | 7/350 |
|
|
[考研] 0817學(xué)碩華東區(qū)求調(diào)劑 +3 | 30660438 2026-03-08 | 3/150 |
|
|
[考研] 08工科 +5 | li李樂成 2026-03-06 | 5/250 |
|
|
[考研] 一志愿:武漢理工,材料工程,英二數(shù)二 總分314 +3 | 2202020125 2026-03-10 | 4/200 |
|
|
[基金申請]
PDF在線壓縮軟件
10+3
|
haxia 2026-03-08 | 4/200 |
|
|
[考研] 求調(diào)劑 一志愿蘇州大學(xué),0856化工323分 | 本科應(yīng)化 | 有專利/競賽/科研助手經(jīng)歷 | +7 | 橙子cyx 2026-03-06 | 9/450 |
|
|
[考研] 復(fù)試調(diào)劑 +6 | 呼呼?~+123456 2026-03-08 | 8/400 |
|
|
[考研] 310 070300化學(xué)求調(diào)劑 +4 | 撲風(fēng)鈴的貓 2026-03-08 | 5/250 |
|
|
[考研] 0703化學(xué)求調(diào)劑 +4 | 很老實(shí)人 2026-03-09 | 4/200 |
|
|
[考研] 288求調(diào)劑(0703)一志愿東北大學(xué) +5 | 好好- 2026-03-07 | 5/250 |
|
|
[考研] 081700學(xué)碩一志愿北京化工大學(xué)數(shù)二英一過六級(jí)有競賽求調(diào)劑 +5 | galaxary 2026-03-07 | 7/350 |
|
|
[考研] 求調(diào)劑 +4 | 呼呼?~+123456 2026-03-06 | 4/200 |
|
|
[考研] 085600,一志愿鄭州大學(xué),280分求調(diào)劑 +7 | Wuqi725 2026-03-05 | 7/350 |
|