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arXiv Computer Vision By Zehua Hao, Fang Liu, Qinliang Wang, Yaoyang Du, Xinyan Huang, Puhua Chen

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

Read the original on arXiv Computer Vision →

The paper introduces G2D, a training‑free framework that combines a discriminative model (CLIP) for broad candidate retrieval with a generative vision‑language model for fine‑grained, image‑grounded verification. By using CLIP’s top‑K shortlist and a structured prior from candidate names and probabilities, G2D focuses generative reasoning on uncertain samples, achieving an average accuracy of 68.85% across eight benchmarks—higher than both CLIP alone (59.35%) and the standalone generative model (63.11%). The approach also adapts to various generator configurations and extends to other models such as DCLIP, WaffleCLIP, and CuPL.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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