Distance Induced Semantic COndition VERification NETwork (DiscoverNet), a unified framework designed to cater to a range of CSL scenarios— supervised CSL (sCSL), weakly-supervised CSL (wsCSL), and semi-supervised CSL (ssCSL). In addition to traditional linear projections, we also introduce a prompt learning technique utilizing transformer encoding layer to create diverse embedding spaces. Our framework incorporates a Condition Match Module (CMM) that dynamically matches different training triplets with corresponding embedding spaces, adapting to varying levels of supervision. We also shed light on existing evaluation biases in wsCSL and introduce two novel criteria for a more robust evaluation. Through extensive experiments and visualizations on benchmark datasets such as UT-Zappos-50 k and Celeb-A, we substantiate the efficacy and interpretability of DiscoverNet.">

Generalized Conditional Similarity Learning via Semantic Matching (original) (raw)

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