主动选视角，让模型看更关键的角度，提升物体识别效果。
Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection
- 根据已有视角预测未知视角图像，选信息量最大的继续观察
- 相比随机选角，分割和重建性能显著提升
- 适合需要多角度理解物体的视觉任务
由于视觉场景中存在遮挡等复杂情况，全面理解往往需要从多个视角观察。现有基于多视角的物体中心学习方法通常采用随机或顺序选择视角，虽适用广泛但未必最优。为此，本文提出一种新的主动视角选择策略：基于已观测视角的信息，预测未知视角的图像，并比较两个视角提取的物体中心表示，选择差异最大的未知视角作为下一步观察目标，以最大化信息增益。实验表明，该方法在多个数据集上显著优于随机视角选择，在分割与重构性能上均有提升。同时，模型能准确预测未知视角图像。
原文摘要 · Abstract (English)
Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.
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