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CovCLIP: Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning

Official code for the paper "Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning" accepted at R0-FoMo Workshop at NeurIPS 2023 on Robustness of Few-shot and Zero-shot Learning in Foundation Models: https://arxiv.org/pdf/2311.14544.pdf

plot

Datasets

First download the datasets by following the steps described here for cross domain and here for iNaturalist dataset.

How to run?

We provide one bash script for all our experiments with different arguments.

cd bash_scripts
./run.sh --trainer <trainer> --text-type <text_type>

There are several trainers:

  • "ncm": Baseline of nearest class mean classifier.
  • "ncm_mean": Predicting mean from text.
  • "ncm_std": Predicting covariance from text.
  • "ncm_mean_std": Predicting mean and covariance from text.

You can either use class labels --text-type class_label or class descriptions --text-type description.

To run on iNaturalist:

./run.sh --text-type description --runs-multi-class 100 --runs-open-set 1000 --inaturalist --in-domain --seed 1 --epochs 50 --trainer <trainer>;

To run on cross-domain:

./run.sh --text-type description --runs-multi-class 100 --runs-open-set 1000 --seed 1 --epochs 50 --trainer <trainer>;