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. Under this setting, pre-trained encoders perform poorly, achieving Sample Success Rates (SSR), a strict margin-based identity discrimination metric, as low as 30.7% and often ranking distractors above true cross-view matches. We address this by learning identity-aware representations on a frozen backbone using a two-tier contrastive objective enforcing the hierarchy: same identity > NearID distractor > random negative. This improves SSR to 99.2%, enhances part-level discrimination by 28.0%, and yields stronger alignment with human judgments on DreamBench++, a human-aligned benchmark for personalization. Project page: https://gorluxor.github.io/NearID/
69cfae0ce02fdd68b91b7c8e
Aleksandar Cvejic
claimed_verified
2026-04-07T08:44:42.673Z
61127c19e0075d7cf22d6bc2
https://cdn-avatars.huggingface.co/v1/production/uploads/61127c19e0075d7cf22d6bc2/IMQzstLtUzT5WUOpUhRXs.jpeg
Aleksandar Cvejic
Aleksandar
user
Aleksandar
69cfae0ce02fdd68b91b7c8f
Rameen Abdal
69cfae0ce02fdd68b91b7c90
Abdelrahman Eldesokey
69cfae0ce02fdd68b91b7c91
Bernard Ghanem
69cfae0ce02fdd68b91b7c92
Peter Wonka
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. Under this setting, pre-trained encoders perform poorly, achieving Sample Success Rates (SSR), a strict margin-based identity discrimination metric, as low as 30.7% and often ranking distractors above true cross-view matches. We address this by learning identity-aware representations on a frozen backbone using a two-tier contrastive objective enforcing the hierarchy: same identity > NearID distractor > random negative. This improves SSR to 99.2%, enhances part-level discrimination by 28.0%, and yields stronger alignment with human judgments on DreamBench++, a human-aligned benchmark for personalization. Project page: https://gorluxor.github.io/NearID/
Researchers developed a novel framework using Near-identity distractors to improve identity-focused vision tasks by creating a dataset and evaluation protocol that better isolates identity from background context, leading to more reliable representations and metrics.
Near-identity distractors vision encoders object identity background context Sample Success Rates contrastive objective frozen backbone DreamBench++ human-aligned benchmark