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Found 7 papers, 2 papers with code
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection
Compared to state-of-the-art methods, our proposed method delivers comparable performance on DOTA-v1. 0 and outperforms by 1. 5 mAP on DOTA-v1. 5, all while significantly reducing the model parameters to 16%.
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction
When a person identifies objects, he or she can think by associating objects to many classes and conclude by taking inter-class relations into account.
Associative Partial Domain Adaptation
Our Associative Partial Domain Adaptation (APDA) utilizes intra-domain association to actively select out non-trivial anomaly samples in each source-private class that sample-level weighting cannot handle.
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization
However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure.
Integrating Multiple Receptive Fields through Grouped Active Convolution
Furthermore, we extend an ACU to a grouped ACU, which can observe multiple receptive fields in one layer.
Constructing Fast Network through Deconstruction of Convolution
To cope with various convolutions, we propose a new shift operation called active shift layer (ASL) that formulates the amount of shift as a learnable function with shift parameters.
Active Convolution: Learning the Shape of Convolution for Image Classification
The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself.
