虎年喜虎劲攻关夺隘皆如虎，春节焕春光阿尔伯塔总是春。[福气虎]CSRA给大家拜年啦~~请大家在此微博下评论区留言自己的新春寄语，CSRA在大年初一(2月1日)通过微博平台给CSCer送年货啦~点赞数最高的三名CSCer及随机抽取的三名CSCer（共六名，截至时间8：00pm 山地时间）获得大统华安康猪咸腊肉320g & 大统华安康猪腊肠家庭装500g。小伙伴们快来送上新春祝福吧~~~ ​​​ https://t.cn/A6iiaLlm

几篇论文实现代码：
《Residual Attention: A Simple but Effective Method for Multi-Label Recognition》(ICCV 2021) GitHub:https:// github.com/Kevinz-code/CSRA [fig2]
《PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric》(ICCV 2021) GitHub:https:// github.com/hjwdzh/PrimitiveNet [fig3]
《SSH: A Self-Supervised Framework for Image Harmonization》(ICCV 2021) GitHub:https:// github.com/VITA-Group/SSHarmonization [fig4]
《Deep Relational Metric Learning》(ICCV 2021) GitHub:https:// github.com/zbr17/DRML [fig5]
《Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
》(EMNLP 2021) GitHub:https:// github.com/ruiqi-zhong/Meta-tuning
《Perceiver IO: A General Architecture for Structured Inputs & Outputs》(2021) GitHub:https:// github.com/esceptico/perceiver-io
《Layout-to-Image Translation with Double Pooling Generative Adversarial Networks》(2021) GitHub:https:// github.com/Ha0Tang/DPGAN
《Fastformer: Additive Attention Can Be All You Need》(2021) GitHub:https:// github.com/wilile26811249/Fastformer-PyTorch [fig1]

AMiner论文推荐：
论文标题：Residual Attention: A Simple but Effective Method for Multi-Label Recognition
论文链接：https://t.cn/A6ITnIBr
一种简单但是有效的多标签图像识别方法，仅用 4 行代码，CSRA 可以在许多不同的预训练模型和数据集上实现一致的改进，而无需任何额外的训练。既易于实现又易于计算，还具有直观的解释和可视化。
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