PulseAugur
EN
LIVE 06:22:39

New benchmark ZeroSight improves zero-shot image retrieval evaluation

Researchers have introduced ZeroSight, a new benchmark designed to evaluate Zero-Shot Composed Image Retrieval (ZS-CIR) more accurately. Existing benchmarks often use data that models have already been trained on, leading to inflated performance metrics. ZeroSight utilizes video-sourced datasets and LLM-assisted captioning to create consistent reference-target pairs, ensuring a true zero-shot scenario. The researchers also propose SC4CIR, a method to identify difficult negative targets and improve retrieval performance. AI

IMPACT Establishes a more rigorous evaluation standard for zero-shot image retrieval, potentially guiding future model development.

RANK_REASON The cluster contains a research paper introducing a new benchmark and method for evaluating a specific AI task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenyu Yang, Zemin Du, Shengsheng Qian, Changsheng Xu ·

    Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets

    arXiv:2606.07032v1 Announce Type: cross Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption without training samples. Existing ZS-CIR datasets often suffer from complete irrele…

  2. arXiv cs.CV TIER_1 English(EN) · Changsheng Xu ·

    Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets

    Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption without training samples. Existing ZS-CIR datasets often suffer from complete irrelevance between reference and target images due to n…