ANFI: Rethinking Neighbor Feature Interaction in Person Re-ID

摘要

In person re-identi_x001C_cation, neighbor-based methods haveachieved signi_x001C_cant success by interacting with neighbor samples to ob-tain more robust representations. However, existing methods rely onlyon a_x001E_nity relations, causing their success to depend heavily on the re-liability of selected neighbors. We _x001C_nd that a_x001E_nity-only interaction of-ten fails in challenging scenarios due to the inevitable presence of noisyneighbors. To enable e_x001B_ective interactions under noisy neighborhoods, werevisit neighbor-based methods under distinct reliability conditions andpropose a novel Adaptive Neighbor Feature Interaction (ANFI)method. The core idea of ANFI is to account for negative e_x001B_ects fromnoisy neighbors, allowing samples to remain distinguishable from falsepositive neighbors. Unlike existing methods, ANFI models not only a_x001E_n-ity relations but also discrepancy relations, and employs sample-wiseadaptive weighting for these two types of relations. Given that capturingnegative e_x001B_ects from noisy neighbors di_x001B_ers signi_x001C_cantly from traditionalrelation learning, we derive discrepancy relations from a new neighbor-hood similarity, which provides more information than pairwise sim-ilarity. In addition, we propose Noisy Relation Supervision (NRS)to train ANFI, gradually injecting robustness to noisy relations into themodel. Extensive experiments conducted under standard, cross-modal,and cross-domain settings, including comparisons with neighbor-basedmethods and re-ranking methods, demonstrate the superiority of ourmethod across various neighbor distributions.