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Found 5 papers, 2 papers with code
A Semi-Paired Approach For Label-to-Image Translation
Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning.
Wavelet-based Unsupervised Label-to-Image Translation
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image.
Ranked #11 on
Image-to-Image Translation
on ADE20K Labels-to-Photos
Multimodal Unsupervised Image-To-Image Translation
Translation
+1
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)
USIS: Unsupervised Semantic Image Synthesis
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask.
