Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning (with Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu and Mehrtash Harandi)

This paper shows how to adapt a convolutional network to a new few-shot task at inference time by making the convolution kernels themselves functions of the task. Dynamic kernels are generated conditioned on both the entire task (episode-level context) and each individual sample, with further per-channel and per-spatial-location adaptation, and frequency-domain information is injected to enrich the adaptation signal. This gives the network a principled way to specialise to a new task without full retraining.

[ECCV 2022 paper]