29 citations · 31 across the 4 of their papers we have counts for
4 papers · 1 filter
Rethinking Generalization in Few-Shot Classification
Markus Hiller, Rongkai Ma, Mehrtash Harandi +1
Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be ac…
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Rongkai Ma, Pengfei Fang, Tom Drummond +1
Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learnin…
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Rongkai Ma, Pengfei Fang, Gil Avraham +4
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot l…
Learning Online for Unified Segmentation and Tracking Models
Tianyu Zhu, Rongkai Ma, Mehrtash Harandi +1
Tracking requires building a discriminative model for the target in the inference stage. An effective way to achieve this is online learning, which can comfortably outperform model…