Search Results for author:
Found 4 papers, 3 papers with code
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T).
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead.
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
More specifically, MTFH exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable.
