Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval

• URL: http://arxiv.org/abs/2308.15273v1
• Date: Tue, 29 Aug 2023 13:02:35 GMT
• Title: Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
• Authors: Seongha Eom, Namgyu Ho, Jaehoon Oh and Se-Young Yun
• Abstract summary: Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability. We propose X-MoRe, a novel inference method comprising two key steps: (1) cross-modal retrieval and (2) modal-confidence-based ensemble. X-MoRe demonstrates robust performance across a diverse set of tasks without the need for additional training.
• Score: 29.838375158101027
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works have attempted to enhance CLIP by fine-tuning on downstream tasks, but these have inadvertently led to performance degradation on unseen classes, thus harming zero-shot generalization. This paper aims to address this challenge by leveraging readily available image-text pairs from an external dataset for cross-modal guidance during inference. To this end, we propose X-MoRe, a novel inference method comprising two key steps: (1) cross-modal retrieval and (2) modal-confidence-based ensemble. Given a query image, we harness the power of CLIP's cross-modal representations to retrieve relevant textual information from an external image-text pair dataset. Then, we assign higher weights to the more reliable modality between the original query image and retrieved text, contributing to the final prediction. X-MoRe demonstrates robust performance across a diverse set of tasks without the need for additional training, showcasing the effectiveness of utilizing cross-modal features to maximize CLIP's zero-shot ability.

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