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Intelligent algorithm for selecting suitable training classes for zero-shot object recognition that capture domain diversity and rarity

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CUB
CUB
 
 
SUN
SUN
 
 
new_zsl_models
new_zsl_models
 
 
scripts
scripts
 
 
utils
utils
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
compute_att_coverage.py
compute_att_coverage.py
 
 
dirac_i.py
dirac_i.py
 
 
load_semantics.py
load_semantics.py
 
 
plots_with_av_heatmap.py
plots_with_av_heatmap.py
 
 
rare_effects.py
rare_effects.py
 
 
seed_construction.py
seed_construction.py
 
 
train_test_split.py
train_test_split.py
 
 

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DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning

Published in ACM Transactions on Multimedia Computing, Communications, and Applications, 2023

Intelligent algorithm for selecting suitable training classes for zero-shot object recognition that capture domain diversity and rarity. Instructions to be uploaded soon.

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Intelligent algorithm for selecting suitable training classes for zero-shot object recognition that capture domain diversity and rarity

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computer-vision image-classification object-recognition zero-shot-learning cub-200-2011 sun-attribute-database

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