Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models

• URL: http://arxiv.org/abs/2108.04024v1
• Date: Mon, 9 Aug 2021 13:25:06 GMT
• Title: Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
• Authors: Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, Stephen Gould
• Abstract summary: We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. We propose CIRPLANT, a transformer based model that leverages rich pre-trained vision-and-language (V&L) knowledge for modifying visual features conditioned on natural language. We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion.
• Score: 41.7254780975984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reasoning in rich image and language contexts. To address this issue, we collect the Compose Image Retrieval on Real-life images (CIRR) dataset, which consists of over 36,000 pairs of crowd-sourced, open-domain images with human-generated modifying text. To extend current methods to the open-domain, we propose CIRPLANT, a transformer based model that leverages rich pre-trained vision-and-language (V&L) knowledge for modifying visual features conditioned on natural language. Retrieval is then done by nearest neighbor lookup on the modified features. We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion. Together with the release of CIRR, we believe this work will inspire further research on composed image retrieval.

Related papers

• Training-free Zero-shot Composed Image Retrieval via Weighted Modality Fusion and Similarity [2.724141845301679]
  Composed image retrieval (CIR) formulates the query as a combination of a reference image and modified text. We introduce a training-free approach for ZS-CIR. Our approach is simple, easy to implement, and its effectiveness is validated through experiments on the FashionIQ and CIRR datasets.
  arXiv  Detail & Related papers  (2024-09-07T21:52:58Z)
• Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval [92.13664084464514]
  The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments. We propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning.
  arXiv  Detail & Related papers  (2024-03-03T07:58:03Z)
• Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking [34.31345844296072]
  Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. Most current composed image retrieval methods follow a supervised learning approach to training on a costly triplet dataset composed of a reference image, modified text, and a corresponding target image. We present a new training-free zero-shot composed image retrieval method which translates the query into explicit human-understandable text.
  arXiv  Detail & Related papers  (2023-12-14T13:31:01Z)
• Vision-by-Language for Training-Free Compositional Image Retrieval [78.60509831598745]
  Compositional Image Retrieval (CIR) aims to retrieve the relevant target image in a database. Recent research sidesteps this need by using large-scale vision-language models (VLMs) We propose to tackle CIR in a training-free manner via Vision-by-Language (CIReVL)
  arXiv  Detail & Related papers  (2023-10-13T17:59:38Z)
• Sentence-level Prompts Benefit Composed Image Retrieval [69.78119883060006]
  Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. We propose to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence-level prompts. Our proposed method performs favorably against the state-of-the-art CIR methods on the Fashion-IQ and CIRR datasets.
  arXiv  Detail & Related papers  (2023-10-09T07:31:44Z)
• BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval [61.803481264081036]
  Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text. We tackle this task by a novel underlinetextbfBottom-up crunderlinetextbfOss-modal underlinetextbfSemantic compounderlinetextbfSition (textbfBOSS) with Hybrid Counterfactual Training framework.
  arXiv  Detail & Related papers  (2022-07-09T07:14:44Z)
• Cross-Modal Retrieval Augmentation for Multi-Modal Classification [61.5253261560224]
  We explore the use of unstructured external knowledge sources of images and their corresponding captions for improving visual question answering. First, we train a novel alignment model for embedding images and captions in the same space, which achieves substantial improvement on image-caption retrieval. Second, we show that retrieval-augmented multi-modal transformers using the trained alignment model improve results on VQA over strong baselines.
  arXiv  Detail & Related papers  (2021-04-16T13:27:45Z)

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.