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Found 3 papers, 3 papers with code
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture
Our approach demonstrates improved performance when using non-object-centric images as pretraining data compared to other methods that train the model to be insensitive to geometric transformation.
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images.
Ranked #1 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Boosting Graph Neural Networks by Injecting Pooling in Message Passing
There has been tremendous success in the field of graph neural networks (GNNs) as a result of the development of the message-passing (MP) layer, which updates the representation of a node by combining it with its neighbors to address variable-size and unordered graphs.
