no code implementations • 23 May 2023 • Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning, Wei Fang, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian
However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.
no code implementations • 22 May 2023 • Munan Ning, Yujia Xie, Dongdong Chen, Zeyin Song, Lu Yuan, Yonghong Tian, Qixiang Ye, Li Yuan
One natural approach is to use caption models to describe each photo in the album, and then use LLMs to summarize and rewrite the generated captions into an engaging story.
1 code implementation • CVPR 2023 • Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng, Li Yuan, Yonghong Tian
However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes.