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Found 4 papers, 1 papers with code
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
However, the powerful Mixup sample-mixing data augmentation cannot be well utilized to address this challenge, as it cannot perform linear interpolation on the unknown labels to construct augmented samples.
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image.
Group Equivariant BEV for 3D Object Detection
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations.
