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Found 3 papers, 0 papers with code
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems.
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
We make the observation that the large gap in performance between two-stage and one-stage FSODs are mainly due to their weak discriminability, which is explained by a small post-fusion receptive field and a small number of foreground samples in the loss function.
Ranked #13 on
Few-Shot Object Detection
on MS-COCO (10-shot)
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity.
Ranked #8 on
Few-Shot Object Detection
on MS-COCO (10-shot)
