You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
- URL: http://arxiv.org/abs/2403.07222v2
- Date: Wed, 20 Mar 2024 19:25:38 GMT
- Title: You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
- Authors: Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song,
- Abstract summary: We introduce a novel compositionality framework, effectively combining sketches and text using pre-trained CLIP models.
Our system extends to novel applications in composed image retrieval, domain transfer, and fine-grained generation.
- Score: 120.49126407479717
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Two primary input modalities prevail in image retrieval: sketch and text. While text is widely used for inter-category retrieval tasks, sketches have been established as the sole preferred modality for fine-grained image retrieval due to their ability to capture intricate visual details. In this paper, we question the reliance on sketches alone for fine-grained image retrieval by simultaneously exploring the fine-grained representation capabilities of both sketch and text, orchestrating a duet between the two. The end result enables precise retrievals previously unattainable, allowing users to pose ever-finer queries and incorporate attributes like colour and contextual cues from text. For this purpose, we introduce a novel compositionality framework, effectively combining sketches and text using pre-trained CLIP models, while eliminating the need for extensive fine-grained textual descriptions. Last but not least, our system extends to novel applications in composed image retrieval, domain attribute transfer, and fine-grained generation, providing solutions for various real-world scenarios.
Related papers
- Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance [17.251982243534144]
LAR-Gen is a novel approach for image inpainting that enables seamless inpainting of masked scene images.
Our approach adopts a coarse-to-fine manner to ensure subject identity preservation and local semantic coherence.
Experiments and varied application scenarios demonstrate the superiority of LAR-Gen in terms of both identity preservation and text semantic consistency.
arXiv Detail & Related papers (2024-03-28T16:07:55Z) - Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval [11.798006331912056]
The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions.
We propose a novel TIPR framework to build fine-grained interactions and alignment between person images and the corresponding texts.
arXiv Detail & Related papers (2023-07-18T08:23:46Z) - Text-based Person Search without Parallel Image-Text Data [52.63433741872629]
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect.
In this paper, we make the first attempt to explore TBPS without parallel image-text data.
arXiv Detail & Related papers (2023-05-22T12:13:08Z) - A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch [63.12810494378133]
We present an end-to-end trainable model for image retrieval using a text description and a sketch as input.
We empirically demonstrate that using an input sketch (even a poorly drawn one) in addition to text considerably increases retrieval recall compared to traditional text-based image retrieval.
arXiv Detail & Related papers (2022-08-05T18:43:37Z) - SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo
and Text [109.69076457732632]
In this paper, we extend scene understanding to include that of human sketch.
We focus on learning a flexible joint embedding that fully supports the optionality" that this complementarity brings.
arXiv Detail & Related papers (2022-04-25T20:58:17Z) - ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and
Implicit Similarity [16.550790981646276]
Current approaches combine the features of each of the two elements of the query into a single representation.
Our work aims at shedding new light on the task by looking at it through the prism of two familiar and related frameworks: text-to-image and image-to-image retrieval.
arXiv Detail & Related papers (2022-03-15T17:29:20Z) - Language Matters: A Weakly Supervised Pre-training Approach for Scene
Text Detection and Spotting [69.77701325270047]
This paper presents a weakly supervised pre-training method that can acquire effective scene text representations.
Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features.
Experiments show that our pre-trained model improves F-score by +2.5% and +4.8% while transferring its weights to other text detection and spotting networks.
arXiv Detail & Related papers (2022-03-08T08:10:45Z) - Telling the What while Pointing the Where: Fine-grained Mouse Trace and
Language Supervision for Improved Image Retrieval [60.24860627782486]
Fine-grained image retrieval often requires the ability to also express the where in the image the content they are looking for is.
In this paper, we describe an image retrieval setup where the user simultaneously describes an image using both spoken natural language (the "what") and mouse traces over an empty canvas (the "where")
Our model is capable of taking this spatial guidance into account, and provides more accurate retrieval results compared to text-only equivalent systems.
arXiv Detail & Related papers (2021-02-09T17:54:34Z) - Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image
Classification and Retrieval [8.317191999275536]
This paper focuses on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval.
We employ a Graph Convolutional Network to perform multi-modal reasoning and obtain relationship-enhanced features by learning a common semantic space between salient objects and text found in an image.
arXiv Detail & Related papers (2020-09-21T12:31:42Z)
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.