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

• URL: http://arxiv.org/abs/2407.12315v2
• Date: Sat, 26 Oct 2024 09:04:25 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.

Related papers

• Shapley Value-based Contrastive Alignment for Multimodal Information Extraction [17.04865437165252]
  We introduce a new paradigm of Image-Context-Text interaction. We propose a novel Shapley Value-based Contrastive Alignment (Shap-CA) method. Our method significantly outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-25T08:15:43Z)
• Learning Modality-agnostic Representation for Semantic Segmentation from Any Modalities [8.517830626176641]
  Any2Seg is a novel framework that can achieve robust segmentation from any combination of modalities in any visual conditions. Experiments on two benchmarks with four modalities demonstrate that Any2Seg achieves the state-of-the-art under the multi-modal setting.
  arXiv  Detail & Related papers  (2024-07-16T03:34:38Z)
• Modality Prompts for Arbitrary Modality Salient Object Detection [57.610000247519196]
  This paper delves into the task of arbitrary modality salient object detection (AM SOD) It aims to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images. A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD.
  arXiv  Detail & Related papers  (2024-05-06T11:02:02Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing [88.6654909354382]
  We present a pure transformer-based framework, dubbed the Flexible Modal Vision Transformer (FM-ViT) for face anti-spoofing. FM-ViT can flexibly target any single-modal (i.e., RGB) attack scenarios with the help of available multi-modal data. Experiments demonstrate that the single model trained based on FM-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin.
  arXiv  Detail & Related papers  (2023-05-05T04:28:48Z)
• Improving Cross-modal Alignment for Text-Guided Image Inpainting [36.1319565907582]
  Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. We propose a novel model for TGII by improving cross-modal alignment. Our model achieves state-of-the-art performance compared with other strong competitors.
  arXiv  Detail & Related papers  (2023-01-26T19:18:27Z)
• Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis [47.29528724322795]
  Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently. Despite significant progress, there are still two major challenges on the way towards robust MSA. We propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR)
  arXiv  Detail & Related papers  (2022-08-16T08:02:30Z)
• Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning [23.472951216815765]
  Key to effective video representations is cross-modal representation learning and fine-grained feature discrimination. In this paper, we enrich intra-modality and cross-modality relations for representation modeling. We enlarge the discriminative power of feature embedding with a hard-pairs guided contrastive learning scheme.
  arXiv  Detail & Related papers  (2022-06-21T07:29:37Z)
• Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing [73.29587731448345]
  We propose a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH) We learn informative representations that can preserve both intra- and inter-modal similarities. The proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.
  arXiv  Detail & Related papers  (2021-12-13T08:58:03Z)
• Learning Multimodal VAEs through Mutual Supervision [72.77685889312889]
  MEME combines information between modalities implicitly through mutual supervision. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes.
  arXiv  Detail & Related papers  (2021-06-23T17:54:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.