| 查看: 7126 | 回复: 9 | ||
[求助]
第一次投SCI（PR Letters）,一个R，一个AQ，求助应该怎么办， 已有6人参与
|
|
本人第一次投SCI，选择了PR letters，（当时听别人说的这个审稿时间比较快，就选了这个）结果是一个R，一个AQ，其中的很多意见都不知道怎么回答， 问题一：从审稿人的意见来看，改后再投还有戏吗？ 问题二：请问如果大改后再投的话，是不是需要将文章的model和算法都要进行修改？ 问题三：如果转投别的期刊，大家有什么好推荐的期刊（我是做CV中的行人再辨识的），影响因子低没有问题，主要是好中，然后审稿期不要太长（一年的那种我等不及啊） 我将审稿意见附在下面，请各位给我出出主意吧，谢了 --------------------------------------------------------------------------------------------------------------------------------------------------- ** {This applies to SUBMITTING AUTHOR Accounts ONLY: You can find any possible ATTACHMENTS FROM THE REVIEWERS by going to the "Manuscript with Decisions" status link in your Author Center and clicking on "view decision letter". They are located at the bottom of decision letter under "Files attached" heading} Dear Mr. Zhao, The reviewing process of your paper submitted to the IEEE Signal Processing Letters is now completed. Comments from the reviewers are attached at the end of this email. (** See note below about attachments). Based on the attached set of reviews, I regret to inform you that I have to decide to REJECT the paper for publication. Two highly qualified reviewers have looked at your manuscript in detail and, in general, they found it not acceptable for publication in its present form. While reviewers found some merit in the developments and experiments, they still find the paper requires a holistic major revision, and clarification of many issues. The first reviewer raises important problems on the approach, while the second reviewer asks for many non-trivial issues to be discussed in the paper. Considering that the decision process for the IEEE Signal Processing Letters is BINARY (papers that need major revisions are not accepted), I regret that I cannot offer you a more positive decision at this point because we do appreciate your interest in publishing in the IEEE Signal Processing Letters. Resubmission of Previously Rejected Manuscripts: Technically, you cannot resubmit a REJECTED manuscript, as it is a REJECTED and CLOSED paper. You would therefore need to submit it as a new manuscript obtaining a new manuscript ID #, following the guidelines in the Author Center (where you would submit your paper to the system) under "RE-SUBMISSION OF A REJECTED MANUSCRIPT" Authors of Rejected manuscripts are allowed to resubmit their manuscripts only once. The Signal Processing Society strongly discourages resubmission of rejected manuscripts more than once. At the time of submission, you will be asked whether you consider your manuscript to be a new submission or a resubmission of an earlier Rejected manuscript. If you choose to submit a new version of your manuscript, you will be asked to submit supporting documents detailing how your new version addresses all of the reviewers' comments. Full details of the resubmission process can be found in the Signal Processing Society “Policy and Procedures Manual” at http://www.signalprocessingsocie ... e/policy-procedure/ Note that resubmitting your manuscript does not guarantee eventual acceptance, and that your resubmission will be subject to re-review by the reviewers before a decision is rendered. Also note that the original Associate Editor who managed the original peer review process is not guaranteed as well. Resubmissions are to be treated as brand new submissions without bias. Sincerely, Prof. Gustau Camps-Valls Associate Editor gcamps@uv.es, gustavo.camps@uv.es * If you have any questions regarding the reviews, please contact the managing Associate Editor who managed the peer review of your paper. Reviewer Comments: Reviewer: 1 Recommendation: R - Reject (Paper Is Not Of Sufficient Quality Or Novelty To Be Published In This Transactions) Comments: The paper presents a method that looks for addressing the problem of matching images that have similar features. They are focused on match images of pedestrians which have been taken with two different cameras. The method is based on learning a specific metric for each datum. In general I see the proposed solution too much complicated, one have to fit a huge amount of parameters. Besides the general parameters: mu, beta, c and P. For each pair of data, one have to find, Wx-> dxK, Wy-> dxK, L-> dxKd. I think that this amount of parameters make the method suffers in overfitting. Although I have my concerns about the method, I would be able to recheck it, if the authors remedied all the issues below. MAJOR ISSUES - Regard the test procedure. Authors say: "At the test time, for a given test image, we find its P-nearest images in the training set and assign the weighting vector of the test image as the linear combination of these P corresponding weighting vectors." This is the critical point of the method. Is it robust for new test images? At least, it does not seem so. For instance, if a test image has not similar images in the training set, the linear combination of the weights of the assigned training images would have no sense. Authors has to justify the robustness of the method for new samples. - Regard the parameters employed. Authors say: "The parameters in our method are set as μ = 0.5, K = 10, β = 2.5, c = 1, P = 5." Authors have to explain which method they have employed to select these values. - It is necessary to present the gradient function for obtaining the solution of the W matrix. - The code of the method has to be publicly available. - Authors have to justify the sentence: "In order to accelerate the optimization process, we design an initial value of weighting matrix as follows." MINOR ISSUES - LCC is not defined in the abstract. - Please change. Recently, a multi-metric based method LAFT [14] is ... -> Recently, a multi-metric based method LAFT [14] was ... - Please change. ...work are presented in Section IV. -> ...work is presented in Section IV. - Page 2. Define \mu before (or just after) equation 5. Additional Questions: 1. Is the topic appropriate for publication in this transaction?: Yes 2. Is the topic important to colleagues working in the field?: Yes Explain: 3. How would you rate the technical novelty of the paper?: Somewhat Novel 4. How would you rate the English usage? : Satisfactory 6. Rate the references: Satisfactory null: Reviewer: 2 Recommendation: AQ - Publish In Minor, Required Changes Comments: Summary: This paper presents a locally adaptive method for person re-identification. Conventional supervised methods for re-id train a single verification model, transform or mahalanobis metric to improve matching accuracy across the views. However, in reality the transform between the two views is likely to be multi-modal, so a single metric is not ideal. This paper builds on the ideas of local coordinate coding and metric learning to build a metric that is adapted to any given pair of points to be matched. The results are competitive with state of the art methods on three benchmark datasets. Overall: + The notion of customising the matching method according to the specific pairs to be matched is powerful but currently under explored in re-id. The main existing approaches are [14], and (un-cited) [A], [B] However this seems to be quite different to both, so is a worthwhile contribution. + The performance is comparable with current state of the art. However, a few issues should be fixed before acceptance: - The dismissal of mixture of experts due to "partitioning the space in a complex way" is a bit too cursory. (Especially since [14] still beats the current work in some settings). - It seems that this paper has 2NxK + dxKd parameters to learn for the weights and the projections respectively, compared to dxd for normal mahalanobis metric learners. This seems like a lot of parameters. Please give some intuition about why over fitting isn't a problem. Especially since MoE is criticised as risk of over fitting. - "modalities" seems to be used to essentially mean camera views. This is confusing, as modality more typically means sensing modalities such as color/texture, image/vision. - P1L59. Is [19] anything to do with multi-metric? Is this the wrong reference? - Around Eq (4), other subscripts are indexes. But I think 'b' in L_{bk} just means basis projection, rather than an index, so maybe use superscript or something else to indicate a basis projection. - Eq(10), shouldn't it be d(x,y) on LHS, not d(\bar{x},\bar{y}) ? - The test time procedure is insufficiently clearly explained. For ease and logic of reading it should be a separate Sec III.C, rather than buried in a paragraph of III.A. Then please explain it in a bit more detail. Why is the test image encoded as NNs in training? (Why not also encoded also in terms of the LCC?) And how are the weights in the linear combination determined? And what is the intuition about the significance of the choice of P? - Citations [A],[B] should be included and discussed/contrasted. - The original LCC paper seemed to learn the anchor points and the weights jointly. Here the anchor is pre-computed by KNN, and then only weights are learned. Some discussion is necessary about what is lost by doing this in terms of the favourable properties/guarantees of LCC. - Sec IV says the feature vector is d=5895 dimensions and K=10. It seems improbable that a Lbar matrix of size 5895 x 58950 can be learned. I suppose there is some missing dimensionality reduction step? Please explain or fill in the missing details. Please check english thoroughly, there are many errors. To mention just a few: - Abstract P1L19-20 makes no sense. Potential to partition? - P1L25 liner -> linear? - P1L37 attention(s) - Various places: "generated by clustering method" is wrong. By a clustering method, or by clustering. - P4L39-40 non-standard english. - P4L20 sufficiently characterise(*) the data - P4L21 even if they come. [A] CVPR'13: Learning Locally-Adaptive Decision Functions for Person Verification. [B] Pattern Recognition 2014: On-the-fly feature importance mining for person re-identification Additional Questions: 1. Is the topic appropriate for publication in this transaction?: Yes 2. Is the topic important to colleagues working in the field?: Yes Explain: 3. How would you rate the technical novelty of the paper?: Somewhat Novel 4. How would you rate the English usage? : Needs improvement 6. Rate the references: Satisfactory |
回复此楼» 猜你喜欢
354求调剂
已经有5人回复
-
材料专业求调剂
已经有3人回复
-
求材料调剂
已经有7人回复
-
一志愿天大材料与化工（085600）总分338
已经有3人回复
-
085600材料与化工
已经有5人回复
-
085600材料与化工调剂 324分
已经有6人回复
-
286求调剂
已经有9人回复
-
焦虑
已经有12人回复
-
344求调剂
已经有6人回复
-
266求调剂
已经有9人回复
» 本主题相关价值贴推荐，对您同样有帮助:
- 第一次投SCI，稿件投出去3天了，一点回音也没有。。。是不是悲剧了？
已经有3人回复
- 投稿一篇SCI三区的文章，第一次编辑说修改，第二次就拒稿了，这是文章不好吗
已经有8人回复
- 第一次写SCI的人必须有一个强大的心脏！！！
已经有130人回复
- 第一次投SCI，当天就被拒稿了。。
已经有77人回复
- 第一次投SCI，被拒，审稿意见求点评
已经有5人回复
- 第一次投稿，不知道投哪里？求推荐~材料专业2-3区SCI就行~
已经有13人回复
- 催化的偏向环境方面，第一次写英文，有哪些国外的SCI相对比较好投？？谢谢！
已经有5人回复
- 第一次投SCI，三个多月返回SCI修改意见：大修！
已经有20人回复
- 第一次投SCI。。关于修改求助。。
已经有20人回复
- 第一次投SCI文章 要求好审稿人
已经有6人回复
- JOSA B这样的状态和审稿意见，有戏吗？第一次搞SCI，心里没底啊！
已经有113人回复
- 第一次写SCI论文，信息类的，投哪个期刊比较好！！多谢各位了！！
已经有15人回复
- 第一次投SCI遇到了一些问题，希望大家能给予帮助，谢谢了。
已经有6人回复
- 第一次投SCI，不知道编辑的这封接收来信是否应该回复
已经有11人回复
- 第一次投稿SCI收到的Decision Letter，另请教问题一个
已经有11人回复
- 在线求助高手，第一次投国外SCI的问题（mater charact）
已经有16人回复
- 第一次写SCI，直接用英语好呢还是写成中文再翻译
已经有41人回复
- 在文后单独列图表，正文中是不是就不要插图表了？（第一次投稿SCI）
已经有9人回复
- 第一次投sci的审稿意见，小虫请教～
已经有14人回复
zj15001
木虫 (小有名气)
- 应助: 14 (小学生)
- 金币: 2998.6
- 散金: 1000
- 红花: 4
- 帖子: 201
- 在线: 91.4小时
- 虫号: 1838548
- 注册: 2012-05-29
- 性别: GG
- 专业: 信号理论与信号处理
2楼2014-08-12 21:02:44
glassman782
铁杆木虫 (知名作家)
- 应助: 1519 (讲师)
- 金币: 13340.3
- 红花: 29
- 帖子: 7313
- 在线: 513.9小时
- 虫号: 468843
- 注册: 2007-11-28
- 专业: 粉末冶金与粉体工程
3楼2014-08-12 21:15:03
xux730
铁杆木虫 (著名写手)
- 应助: 1719 (讲师)
- 金币: 10740.4
- 散金: 50
- 红花: 43
- 帖子: 2947
- 在线: 336.5小时
- 虫号: 1374299
- 注册: 2011-08-20
- 专业: 细胞周期与调控
4楼2014-08-12 22:14:20
zkk1222
铁杆木虫 (著名写手)
- 应助: 258 (大学生)
- 金币: 6989.9
- 散金: 1135
- 红花: 17
- 帖子: 2165
- 在线: 250.4小时
- 虫号: 1427489
- 注册: 2011-10-04
- 性别: GG
- 专业: 天然有机化学
5楼2014-08-12 22:26:46
6楼2014-08-13 10:10:54
7楼2014-10-01 15:44:03
8楼2015-01-30 22:08:40
误解1990
木虫 (正式写手)
- 应助: 38 (小学生)
- 金币: 2092.4
- 红花: 7
- 帖子: 656
- 在线: 171.4小时
- 虫号: 1170093
- 注册: 2010-12-14
- 性别: GG
- 专业: 城乡规划
9楼2015-01-30 23:45:54
yuanmd
至尊木虫 (知名作家)
衔头义定自
- 应助: 58 (初中生)
- 金币: 9949.2
- 散金: 3502
- 红花: 27
- 帖子: 5262
- 在线: 641.6小时
- 虫号: 1148791
- 注册: 2010-11-16
- 专业: 模式识别
10楼2015-07-02 23:31:44
