cid 59300032dc87de4ca8bdcef2cf4397c38bbc949fe0a185b6b86eb70f620f9c8c · original · open access · via openalex
Title: POPEN: Preference-Based Optimization and Ensemble for LVLM-Based Reasoning Segmentation Authors: Lanyun Zhu; Tianrun Chen; Qianxiong Xu; Xuanyi Liu; Deyi Ji; Haiyang Wu; De Wen Soh; Jun Liu Venue: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings Year: 2025 Canonical: doi:10.1109/cvpr52734.2025.02814 URL: https://doi.org/10.1109/cvpr52734.2025.02814 Collector: openalex OA: true ABSTRACT Existing LVLM-based reasoning segmentation methods often suffer from imprecise segmentation results and hallucinations in their text responses. This paper introduces POPEN, a novel framework designed to address these issues and achieve improved results. POPEN includes a preference-based optimization method to finetune the LVLM, aligning it more closely with human preferences and thereby generating better text responses and segmentation results. Additionally, POPEN introduces a preference-based ensemble method for inference, which integrates multiple outputs from the LVLM using a preference-score-based attention mechanism for refinement. To better adapt to the segmentation task, we incorporate several task-specific designs in our POPEN framework, including a new approach for collecting segmentation preference data with a curriculum learning mechanism, and a novel preference optimization loss to refine the segmentation capability of the LVLM. Experiments demonstrate that our method achieves state-of-the-art performance in reasoning segmentation, exhibiting minimal hallucination in text responses and the highest segmentation accuracy compared to previous advanced methods like LISA and PixelLM. Project page is here.