# Group Membership Prediction Source: [Group Membership Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:12:29.814Z Authors: Ziming Zhang, Yuting Chen, Venkatesh Saligrama Published: 2015-09-16T01:22:40.000Z ## Abstract The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\\it familial} relationship. In this context we propose a novel probability model and introduce latent {\\em view-specific} and {\\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Kinship Verification on TSKinFace (test)](): Kinship Verification; TSKinFace (test); FD: 88.5. - [Kinship Verification on TSKinFace](): Kinship Verification; TSKinFace; Accuracy (FM-S): 90.6. ## Original sources and implementation [Original paper]() [PDF]()