research
∙
03/02/2023
A Meta-Learning Approach to Predicting Performance and Data Requirements
We propose an approach to estimate the number of samples required for a ...
share
research
∙
09/13/2022
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Annotating bounding boxes for object detection is expensive, time-consum...
research
∙
07/22/2022
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setti...
research
∙
04/07/2022
Class-Incremental Learning with Strong Pre-trained Models
Class-incremental learning (CIL) has been widely studied under the setti...
research
∙
03/30/2022
Omni-DETR: Omni-Supervised Object Detection with Transformers
We consider the problem of omni-supervised object detection, which can u...
research
∙
04/30/2020
Out-of-the-box channel pruned networks
In the last decade convolutional neural networks have become gargantuan....
research
∙
05/29/2019