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Found 4 papers, 0 papers with code
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Our contribution is three-fold: (1) we design a standalone lightweight generator with (2) class-wise heads (3) to generate and replay diverse instance-level base features to the RoI head while finetuning on the novel data.
Data-free Knowledge Distillation
Few-Shot Object Detection
+2
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)
Few-Shot Object Detection in Unseen Domains
First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes.
