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smallbug2000木蟲 (著名寫手)
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[求助]
哥的論文大修，哥很郁悶，哥不想改了
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投了一篇論文到計算機視覺相關的雜志，最近返回結果，三個審稿人，其中兩個審稿人意見較少。另一個覺得算法的創(chuàng)新性不夠，給了大修，悲劇的是這篇論文之前投到CVPR去，也是這個審稿人審的（CVPR據(jù)了我），因為審稿意見基本一樣，故可以斷定是同一審稿人。最終編輯給了大修，但是我很絕望，因為給大修的審稿人從一開始就不認可我的算法，我想既然我認真修改了，可能還是會被他據(jù)。將編輯及大修審稿人意見附后，在此向各位大牛咨詢兩件事：1）該論文是否值得大修，修后被據(jù)的概率多大？2）如果大修，如果對付審稿人這么尖銳的問題。在些感謝，對于好的建議發(fā)放金幣。 Dear**, Reviewers have now commented on your paper. You will see that they are advising that you revise your manuscript. If you are prepared to undertake the work required, I would be pleased to reconsider my decision. The reviewers' comments can be found at the end of this email or can be accessed by following the provided link. Reviewers' comments: The paper was reviewed by three experts on the topic. The reviewers agree that the paper discusses an important problem worth pursuing. They, however, also unanimously point out short comings of the paper, most important of which is the lack of thorough theoretical and experimental validation of the claims as well as references to prior work. The paper needs major revision to be considered for publication. If the authors decide to submit a major revision, please make sure to address the points raised by the reviewers. Reviewer #2: : This paper deals with the problem of visual tracking with irregular object motion. The particle set shift approach based on analytic optimization is proposed to deal with the incorrect state dynamic model. Particles are first sampled by the state dynamic model and they are moved to higher likelihood regions by newton optimization by maximizing likelihoods. The efficacy of the proposed approach is demonstrated via experiments with real sequences. - Positive points: Practically effective approach - Negative points: Not novel approach, rather heuristic, not convincing experimental results : The main problem of this paper is the proposed approach is not novel. The proposed approach is quite similar to [18] except the fact that the proposed approach used newton optimization instead of mean shift. : The proposed approach is rather heuristic. In the algorithm, particle weights are only proportional to likelihood, and this holds true for SIR particle filter where particles are sampled from state dynamic model. However, since particles are moved to higher likelihoods artificially, the weights determined from likelihoods will no longer correctly represent the true posterior. The proposed approach is rather similar to the particle swarm optimization-based tracking, e.g., "" by X. ** et al in CVPR 2008. Another problem of the proposed approach is that it might result in worse tracking results when there are appearance changes caused by various issues such as pose and lighting changes. : Since the proposed work is based on particle filter, the optimal importance functions used for visual tracking are also relevant. If we can use the optimal importance function, the irregular motion can be handled at least partially. Thus the following papers should be cited and commented as related work: - : The supplementary video result is too limited. Only a result for a single short sequence is not sufficient to support the validity of the proposed approach. Why is there no rotational motion in the results? The paper says that the state is translation, scale, and rotation, but there is no rotational motion in the results in the paper and video. : There must be cases that all particles are outside the basin of convergence. Since the particle are shifted by local optimization, particles will diverge from the optimal positions and tracking will fail. In the proposed approach, there is no consideration of this possibility. : The optimization will increase the computational complexity considerably. It is necessary to compare the other algorithms under the same computational time, e.g., proposed framework with 100 particles and standard particle filter with 1000 particles. My current recommendation is major revision. If the authors want to make the paper accepted in spite of the limited novelty and contribution, I think the followings should be addressed in the revision: - Additional experiments with object appearance changes like illumination - Addition of a mechanism to deal with the local optima problems with additional experiments with related videos - Comparison under the same computational complexity for different tracking algorithms [ Last edited by smallbug2000 on 2012-9-6 at 09:52 ] |
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smallbug2000
木蟲 (著名寫手)
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9樓2012-09-06 18:53:30
carsonwen
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2樓2012-09-06 10:30:45
flywithyu
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【答案】應助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感謝參與，應助指數(shù) +1
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-06 14:45:35
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-06 17:37:34
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-11 19:27:09
smallbug2000: 金幣+4, ★★★★★最佳答案 2012-09-13 14:47:15
感謝參與，應助指數(shù) +1
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-06 14:45:35
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-06 17:37:34
smallbug2000: 金幣+2, ★★★很有幫助 2012-09-11 19:27:09
smallbug2000: 金幣+4, ★★★★★最佳答案 2012-09-13 14:47:15
|
這個修改意見不算多的，具體就是： 1、增加實驗結果以驗證你方法的有效性；（這個只要有數(shù)據(jù)，應該好辦） 2、局部最優(yōu)問題，這個較難回答，一種你找到方法；另外一種就是通過實驗來說明； 3、與不同算法的比較。首先找找網(wǎng)上有沒有現(xiàn)成的能出結果的代碼吧；實在不行只有自己實現(xiàn)；話說實驗室如果有做換個方向的話應該是有一定基礎吧。 強烈建議：按照專家意見修改，不要回避，最終你會發(fā)現(xiàn)有收獲的！！  |
3樓2012-09-06 12:27:52
wangzhipengj
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4樓2012-09-06 13:05:39
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