GESS: Multi-cue Guided Local Feature Learning via Geometric and Semantic Synergy

ArXi:2604.05359v1 Announce Type: new Robust local feature detection and description are foundational tasks in computer vision. Existing methods primarily rely on single appearance cues for modeling, leading to unstable keypoints and insufficient descriptor discriminability. In this paper, we propose a multi-cue guided local feature learning framework that leverages semantic and geometric cues to synergistically enhance detection robustness and descriptor discriminability.