NearID: Identity Representation Learning via Near-identity Distractors
When evaluating identity-focused tasks such as personalized generation and image editing, existing vision encoders entangle object identity with background context, leading to unreliable representations and metrics. We introduce the first principled framework to address this vulnerability using Near-identity (NearID) distractors, where semantically similar but distinct instances are placed on the exact same background as a reference image, eliminating contextual shortcuts and isolating identity as the sole discriminative signal. Based on this principle, we present the NearID dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation protocol....
| Authors | Aleksandar Cvejic, Rameen Abdal, Abdelrahman Eldesokey et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 4 August 2026 |
Models and datasets referencing this paper
- Aleksandar/NearID-Qwen_1328Dataset
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