Supplementary material Re-ranking for image retrieval and transductive few-shot classification Xi Shen 1, Y ang Xiao

PR, and results with different number of neighbors N are provided in Section 3. Hard training data sampling The main difficulty is that there is no standard clean training set. In Table 1, we provide the analysis of hyper-parameters in our SSR on rOxford5K and rParis6K. Additionally, we also study the impact of number of neighbors N. We observe that on rOxford5K Note that bold numbers are reported in the paper. To combine QE and SSR, we directly apply SSR to the retrieved samples given by QE. As we can see, in most cases, our SSR can again improve the performance of QEs.