GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing

ICLR 2026 Conference Submission432 Authors

Keywords: Generic Object Tracking, Model Editing, Model Prediction, Visual Geometry, Null-Space

TL;DR: We develop an online model-editing strategy for a generic object tracker that adaptively fuses geometric information while preserving the semantic knowledge learned by the tracker.

Abstract:

Human perception for effective object tracking in a 2D video stream arises from the implicit use of prior 3D knowledge combined with semantic reasoning.
In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings while neglecting 3D geometric cues, which makes them susceptible to partial occlusion, distractors, and variations in geometry and appearance.
To address this limitation, we introduce GOT-Edit, an online cross-modality model editing approach that integrates geometry-aware cues into a generic object tracker from a 2D video stream.
Our approach leverages features from a pre-trained Visual Geometry Grounded Transformer to enable geometric cue inference from only a few 2D images.
To tackle the challenge of seamlessly combining geometry and semantics, GOT-Edit performs online model editing with null-space constrained updates that incorporate geometric information while preserving semantic discrimination, yielding consistently better performance across diverse scenarios.
Extensive experiments on multiple GOT benchmarks demonstrate that GOT-Edit achieves superior robustness and accuracy, particularly under occlusion and clutter, establishing a new paradigm for combining 2D semantics with 3D geometric reasoning for generic object tracking.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 432