利用增强异常对比学习进行预测推理以处理组合视觉关系
Predictive Reasoning with Augmented Anomaly Contrastive Learning for Compositional Visual Relations
AI总结：
PR-A$^2$CL通过增强异常对比学习和预测验证范式，有效提升了组合视觉关系推理任务的性能。
摘要：
尽管对于简单类比的视觉推理已受到广泛关注，但组合视觉关系（CVR）由于其更大的复杂性仍相对未被探索。为解决CVR任务，我们提出预测推理与增强异常对比学习（PR-A$^2$CL），即在给定三张遵循相同组合规则的图像时识别出异常图像。为了解决建模大量组合规则的挑战，设计了增强异常对比学习，通过最大化正常实例之间的相似性同时最小化正常实例与异常异常值之间的相似性，来提炼具有区分性和通用性的特征。更重要的是，引入了基于规则推理的预测和验证范式，其中一系列预测异常推理块（PARBs）迭代利用四张图像中的三张特征来预测剩余一张的特征。在随后的验证阶段，PARBs逐步定位出归因于底层规则的具体差异。在SVRT、CVR和MC$^2$R数据集上的实验结果表明，PR-A$^2$CL显著优于最先进的推理模型。
英文摘要：
While visual reasoning for simple analogies has received significant attention, compositional visual relations (CVR) remain relatively unexplored due to their greater complexity. To solve CVR tasks, we propose Predictive Reasoning with Augmented Anomaly Contrastive Learning (PR-A$^2$CL), \ie, to identify an outlier image given three other images that follow the same compositional rules. To address the challenge of modelling abundant compositional rules, an Augmented Anomaly Contrastive Learning is designed to distil discriminative and generalizable features by maximizing similarity among normal instances while minimizing similarity between normal and anomalous outliers. More importantly, a predict-and-verify paradigm is introduced for rule-based reasoning, in which a series of Predictive Anomaly Reasoning Blocks (PARBs) iteratively leverage features from three out of the four images to predict those of the remaining one. Throughout the subsequent verification stage, the PARBs progressively pinpoint the specific discrepancies attributable to the underlying rules. Experimental results on SVRT, CVR and MC$^2$R datasets show that PR-A$^2$CL significantly outperforms state-of-the-art reasoning models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
