ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map

• URL: http://arxiv.org/abs/2407.12315v1
• Date: Wed, 17 Jul 2024 04:49:56 GMT
• Title: ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map
• Authors: Yilin Ye, Shishi Xiao, Xingchen Zeng, Wei Zeng,
• Abstract summary: We design ModalChorus, an interactive system for visual probing and alignment of multi-modal embeddings. ModalChorus primarily offers a two-stage process: 1) embedding probing with Modal Fusion Map (MFM), a novel dimensionality reduction method. Case studies reveal that ModalChorus can facilitate intuitive discovery of misalignment and efficient re-alignment in scenarios ranging from zero-shot classification to cross-modal retrieval and generation.
• Score: 1.6570772838074355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal embeddings form the foundation for vision-language models, such as CLIP embeddings, the most widely used text-image embeddings. However, these embeddings are vulnerable to subtle misalignment of cross-modal features, resulting in decreased model performance and diminished generalization. To address this problem, we design ModalChorus, an interactive system for visual probing and alignment of multi-modal embeddings. ModalChorus primarily offers a two-stage process: 1) embedding probing with Modal Fusion Map (MFM), a novel parametric dimensionality reduction method that integrates both metric and nonmetric objectives to enhance modality fusion; and 2) embedding alignment that allows users to interactively articulate intentions for both point-set and set-set alignments. Quantitative and qualitative comparisons for CLIP embeddings with existing dimensionality reduction (e.g., t-SNE and MDS) and data fusion (e.g., data context map) methods demonstrate the advantages of MFM in showcasing cross-modal features over common vision-language datasets. Case studies reveal that ModalChorus can facilitate intuitive discovery of misalignment and efficient re-alignment in scenarios ranging from zero-shot classification to cross-modal retrieval and generation.

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