提出更高效奇偶检测模型，参数减少1/3，训练提速3倍。
Efficient Odd-One-Out Anomaly Detection
- 基于DINO架构压缩模型，降低参数量和训练时间。
- 在多对象场景中保持竞争力，提升奇偶异常检测效率。
- 适合关注视觉推理与模型效率的开发者参考。
近期提出的奇偶异常检测任务要求从多对象场景中识别出异常实例。该问题对现代深度学习模型构成挑战，需具备跨多视角的空间推理能力及上下文关系理解能力，以应对不同物体类别与布局的变化。我们强调在设计此类模型时必须兼顾效率。为此，提出一种基于DINO的模型，相比当前最优方法，参数量减少三分之一，训练时间缩短为三倍，同时保持竞争性性能。实验还引入了多模态大语言模型基线，揭示其在结构化视觉推理任务中的当前局限。项目页面见 https://silviochito.github.io/EfficientOddOneOut/
原文摘要 · Abstract (English)
The recently introduced odd-one-out anomaly detection task involves identifying the odd-looking instances within a multi-object scene. This problem presents several challenges for modern deep learning models, demanding spatial reasoning across multiple views and relational reasoning to understand context and generalize across varying object categories and layouts. We argue that these challenges must be addressed with efficiency in mind. To this end, we propose a DINO-based model that reduces the number of parameters by one third and shortens training time by a factor of three compared to the current state-of-the-art, while maintaining competitive performance. Our experimental evaluation also introduces a Multimodal Large Language Model baseline, providing insights into its current limitations in structured visual reasoning tasks. The project page can be found at https://silviochito.github.io/EfficientOddOneOut/
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