Composing Object Relations and Attributes for Image-Text Matching

• URL: http://arxiv.org/abs/2406.11820v1
• Date: Mon, 17 Jun 2024 17:56:01 GMT
• Title: Composing Object Relations and Attributes for Image-Text Matching
• Authors: Khoi Pham, Chuong Huynh, Ser-Nam Lim, Abhinav Shrivastava,
• Abstract summary: This work introduces a dual-encoder image-text matching model, leveraging a scene graph to represent captions with nodes for objects and attributes interconnected by relational edges. Our model efficiently encodes object-attribute and object-object semantic relations, resulting in a robust and fast-performing system.
• Score: 70.47747937665987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the visual semantic embedding problem for image-text matching. Most existing work utilizes a tailored cross-attention mechanism to perform local alignment across the two image and text modalities. This is computationally expensive, even though it is more powerful than the unimodal dual-encoder approach. This work introduces a dual-encoder image-text matching model, leveraging a scene graph to represent captions with nodes for objects and attributes interconnected by relational edges. Utilizing a graph attention network, our model efficiently encodes object-attribute and object-object semantic relations, resulting in a robust and fast-performing system. Representing caption as a scene graph offers the ability to utilize the strong relational inductive bias of graph neural networks to learn object-attribute and object-object relations effectively. To train the model, we propose losses that align the image and caption both at the holistic level (image-caption) and the local level (image-object entity), which we show is key to the success of the model. Our model is termed Composition model for Object Relations and Attributes, CORA. Experimental results on two prominent image-text retrieval benchmarks, Flickr30K and MSCOCO, demonstrate that CORA outperforms existing state-of-the-art computationally expensive cross-attention methods regarding recall score while achieving fast computation speed of the dual encoder.

Related papers

• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality [50.48859793121308]
  Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning. Recent research has highlighted severe limitations in their ability to perform compositional reasoning over objects, attributes, and relations.
  arXiv  Detail & Related papers  (2023-05-23T08:28:38Z)
• Image Semantic Relation Generation [0.76146285961466]
  Scene graphs can distil complex image information and correct the bias of visual models using semantic-level relations. In this work, we introduce image semantic relation generation (ISRG), a simple but effective image-to-text model.
  arXiv  Detail & Related papers  (2022-10-19T16:15:19Z)
• Relationformer: A Unified Framework for Image-to-Graph Generation [18.832626244362075]
  This work proposes a unified one-stage transformer-based framework, namely Relationformer, that jointly predicts objects and their relations. We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly. We achieve state-of-the-art performance on multiple, diverse and multi-domain datasets.
  arXiv  Detail & Related papers  (2022-03-19T00:36:59Z)
• ReFormer: The Relational Transformer for Image Captioning [12.184772369145014]
  Image captioning is shown to be able to achieve a better performance by using scene graphs to represent the relations of objects in the image. We propose a novel architecture ReFormer to generate features with relation information embedded. Our model significantly outperforms state-of-the-art methods on image captioning and scene graph generation.
  arXiv  Detail & Related papers  (2021-07-29T17:03:36Z)
• Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph [96.95815946327079]
  It is difficult to learn the association between named entities and visual cues due to the long-tail distribution of named entities. We propose a novel approach that constructs a multi-modal knowledge graph to associate the visual objects with named entities.
  arXiv  Detail & Related papers  (2021-07-26T05:50:41Z)
• Tensor Composition Net for Visual Relationship Prediction [115.14829858763399]
  We present a novel Composition Network (TCN) to predict visual relationships in images. The key idea of our TCN is to exploit the low rank property of the visual relationship tensor. We show our TCN's image-level visual relationship prediction provides a simple and efficient mechanism for relation-based image retrieval.
  arXiv  Detail & Related papers  (2020-12-10T06:27:20Z)
• Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching [102.62343739435289]
  Existing image-text matching approaches infer the similarity of an image-text pair by capturing and aggregating the affinities between the text and each independent object of the image. We propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN) Our model achieves the state-of-the-art performance on Flickr30K dataset and competitive performance on MS-COCO dataset.
  arXiv  Detail & Related papers  (2020-02-20T00:51:01Z)

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.