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Adapter的前世今生
Published:
Introduction
并行计算心得和总结
Published:
基于openmp编程模型，先从粗粒度的层面对算法进行优化，此时就需要考验对算法本身的理解程度和基本的编程算法能力，此时要多次迭代，正确分析算法复杂度；后续进入细粒度层面优化，主要是在计算和访存之间做trade-off，向量化是一个很强大的工具，向量寄存器yyds!另外有些开放的问题，比如循环展开最合适的次数等等…
portfolio
Long-tail Cross Modal Hashing
It’s an introduction video to the paper presented at the AAAI-23 Conference 
publications
Long-tail Cross Modal Hashing (Zijun Gao, Jun Wang, Guoxian Yu, Zhongmin Yan, Carlotta Domeniconi, Jinglin Zhang)
Published in 37th AAAI Conference on Artificial Intelligence (AAAI) (CCF A), In Print., 2022
Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-modal data. Furthermore, CMH methods mostly mine the commonality of multi-modal data to learn hash codes, which may override tail labels encoded by the individuality of respective modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities. Then it dynamically combines the individuality and commonality with direct features extracted from respective modalities to create meta features that enrich the representation of tail labels, and binaries meta features to generate hash codes. LtCMH significantly outperforms state-of-the-art baselines on long-tail datasets and holds a better (or comparable) performance on datasets with balanced labels.
Recommended citation: http://zjgao02.github.io/files/2211.15162.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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