Reproducibility Study of "CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification"
Abstract: This report is a reproducibility study of the paper "CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification" published at ICCV 2023. Our report makes the following contributions: (1) We provide a reproducible, well commented and open-sourced code implementation for the entire method specified in the original paper. (2) We try to verify the effectiveness of the novel aggregation strategy which uses the CLIP model to initialize the pseudo labels for the subsequent unsupervised multi-label image classification task. (3) We try to verify the effectiveness of the gradient-alignment training method specified in the original paper, which is used to update the network parameters and pseudo labels.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Antoni_B._Chan1
Submission Number: 2249
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