==================================== 咳咳咳 由于科研的直接对象就是video sequence,所以,如何更好地提取spatial-temporal information至关重要. so,总结了一下以前看过的,包括现在正在复现的paper 中的idea. 1. LSTM L. Jiang, M. Xu, and Z. Wang. Predicting video saliency with object-to-motion CNN and two-laye…
10 Exploring Temporal Information for Dynamic Network Embedding 5 link:https://scholar.google.com.sg/scholar_url?url=https://ieeexplore.ieee.org/abstract/document/9242309/&hl=zh-TW&sa=X&ei=ZiiOYp6gEpT0yASct56wBQ&scisig=AAGBfm3bQgwV0icZGtwl…
阶段性总结 Boolean retrieval 单词搜索 [Qword1 and Qword2] O(x+y) [Qword1 and Qword2]- 改进: Galloping Search O(2a*log2(b/a)) [Qword1 and not Qword2] O(m*log2n) [Qword1 or not Qword2] O(m+n) [Qword1 and Qword2 and Qword3 and ...…
摘要 文章针对修复坏波段(AQUA B6),恢复条带损失,恢复云污染提出了一个深度学习网络结构,他说 To date, to the best of our knowledge, no studies investigating CNNs for the reconstruction of missing information in remote sensing imagery have made full use of the feature mining and nonlinear exp…