From Latent to Engine Manifolds: Analyzing ImageBind's Multimodal Embedding Space

• URL: http://arxiv.org/abs/2409.10528v1
• Date: Fri, 30 Aug 2024 17:16:33 GMT
• Title: From Latent to Engine Manifolds: Analyzing ImageBind's Multimodal Embedding Space
• Authors: Andrew Hamara, Pablo Rivas,
• Abstract summary: We propose a simplistic embedding fusion workflow that aims to capture the overlapping information of image/text pairs. After storing such fused embeddings in a vector database, we experiment with dimensionality reduction and provide empirical evidence to convey the semantic quality of the joint embeddings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study investigates ImageBind's ability to generate meaningful fused multimodal embeddings for online auto parts listings. We propose a simplistic embedding fusion workflow that aims to capture the overlapping information of image/text pairs, ultimately combining the semantics of a post into a joint embedding. After storing such fused embeddings in a vector database, we experiment with dimensionality reduction and provide empirical evidence to convey the semantic quality of the joint embeddings by clustering and examining the posts nearest to each cluster centroid. Additionally, our initial findings with ImageBind's emergent zero-shot cross-modal retrieval suggest that pure audio embeddings can correlate with semantically similar marketplace listings, indicating potential avenues for future research.

Related papers

• Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation [66.15246197473897]
  Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation. We propose a textbfMulti-textbfinteractive textbfFeature learning architecture for image fusion and textbfSegmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:03:58Z)
• An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection [59.02821429555375]
  This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IVIF) enables hard-to-find objects apparent. multimodal salient object detection (SOD) accurately delineates the precise spatial location of objects within the picture.
  arXiv  Detail & Related papers  (2023-05-17T06:48:35Z)
• ImageBind: One Embedding Space To Bind Them All [41.46167013891263]
  ImageBind is an approach to learn a joint embedding across six different modalities. We show that only image-paired data is sufficient to bind the modalities together.
  arXiv  Detail & Related papers  (2023-05-09T17:59:07Z)
• Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval [55.21569389894215]
  We propose a cross-attention framework for Vision Transformers (XModalViT) that fuses modality-specific information instead of discarding them. Our framework first maps paired datapoints from the individual photo and sketch modalities to fused representations that unify information from both modalities. We then decouple the input space of the aforementioned modality fusion network into independent encoders of the individual modalities via contrastive and relational cross-modal knowledge distillation.
  arXiv  Detail & Related papers  (2022-10-19T11:50:14Z)
• Comprehensive Saliency Fusion for Object Co-segmentation [3.908842679355254]
  Saliency fusion has been one of the promising ways to carry out object co-segmentation. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advances in deep learning for the saliency extraction and correspondence processes.
  arXiv  Detail & Related papers  (2022-01-30T14:22:58Z)
• Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection [9.356003255288417]
  This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module then reweighs the channel features to enhance the consistency of different feature maps.
  arXiv  Detail & Related papers  (2021-12-24T15:07:47Z)
• Compositional Sketch Search [91.84489055347585]
  We present an algorithm for searching image collections using free-hand sketches. We exploit drawings as a concise and intuitive representation for specifying entire scene compositions.
  arXiv  Detail & Related papers  (2021-06-15T09:38:09Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)
• SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation [14.419517737536706]
  Recent deep neural network based FSS methods leverage high-dimensional feature similarity between the foreground features of the support images and the query image features. We propose to jointly predict the support and query masks to force the support features to share characteristics with the query features. Our method achieves state-of-the-art results for one-shot and five-shot segmentation on the PASCAL-5i dataset.
  arXiv  Detail & Related papers  (2020-04-30T17:56:48Z)
• Gradient-Induced Co-Saliency Detection [81.54194063218216]
  Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection method.
  arXiv  Detail & Related papers  (2020-04-28T08:40:55Z)

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.