Anuj Garg

This work proposes a methodology for training CIR retrievers on graded relevance, consisting of a VLM to curate training data, an iterative relevance-feedback loop that expands the training set by mining hard negatives from the in-training retriever, and a hierarchy-aware angular objective to train the retriever directly on the graded labels rather than collapsing them to a binary split.