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ReCoG: Relational Concept Graph for Training-Free Personalization

Changseon Yu, Hyeokjun Kweon · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31282 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

虽然最近的获取者基于定义的方法以描述性证据丰富每个概念,他们大多在孤立中对待概念,所以获取的信息可以是可信的,但没有歧视性的,特别是当个性化概念集包含高度混淆的,细粒类别时。我们认为个性化本质上是相对的:正确的识别取决于一个概念和它最接近的替代品之间的具体区别。为此目的,我们提出了ReCoG,一个可信的,但没有歧视的概念,特别是在个性化概念集中包含高度混淆的,细粒类别。我们认为个性化具有广泛的边缘,它明确地捕捉了不同概念之间的区别性概念,在VLM在构建过程中被冻结的最佳物质测试。在项目中,FGG的最大

Machine translation (Meta M2M100); the English original follows.

Abstract

Training-free personalization adapts a frozen vision-language model (VLM) to recognize user-defined concepts from only a few exemplars by retrieving concept entries at inference time. While recent retriever-based methods enrich each concept with descriptive evidence, they largely treat concepts in isolation, so the retrieved information can be plausible yet non-discriminative, especially when the personalized concept set contains highly confusable, fine-grained categories. We argue that personalization is inherently relational: correct recognition depends on the specific distinctions between a concept and its nearest alternatives. To this end, we propose ReCoG, a Relational Concept Graph for retrieval-augmented personalization. ReCoG represents concepts as nodes and augments them with directed edges that explicitly capture discriminative cues between concept pairs, elicited by a frozen VLM during database construction. At inference, ReCoG retrieves a shortlist and resolves ambiguity via explicit pairwise comparisons conditioned on the corresponding relational edges, selecting the concept that consistently wins against competing candidates. We also introduce FINGER benchmark to test personalized recognition on confusable concept sets. Across FINGER, ReCoG substantially outperforms prior methods, with the largest gains in the most confusable regimes. ReCoG also achieves consistently best performance on established personalization benchmarks including captioning and VQA, confirming that relational evidence is broadly beneficial beyond fine-grained settings. Project Page: https://csy-98.github.io/ReCoG/

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Topics: Computer science, Personalization, Ambiguity, Pairwise comparison, Discriminative model, Artificial intelligence

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