Unleashing the Imagination of Text: A Novel Framework for Text-to-image Person Retrieval via Exploring the Power of Words

• URL: http://arxiv.org/abs/2307.09059v1
• Date: Tue, 18 Jul 2023 08:23:46 GMT
• Title: Unleashing the Imagination of Text: A Novel Framework for Text-to-image Person Retrieval via Exploring the Power of Words
• Authors: Delong Liu, Haiwen Li
• Abstract summary: We propose a novel framework to explore the power of words in sentences. The framework employs the pre-trained full CLIP model as a dual encoder for the images and texts. We introduce a cross-modal triplet loss tailored for handling hard samples, enhancing the model's ability to distinguish minor differences.
• Score: 0.951828574518325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of Text-to-image person retrieval is to retrieve person images from a large gallery that match the given textual descriptions. The main challenge of this task lies in the significant differences in information representation between the visual and textual modalities. The textual modality conveys abstract and precise information through vocabulary and grammatical structures, while the visual modality conveys concrete and intuitive information through images. To fully leverage the expressive power of textual representations, it is essential to accurately map abstract textual descriptions to specific images. To address this issue, we propose a novel framework to Unleash the Imagination of Text (UIT) in text-to-image person retrieval, aiming to fully explore the power of words in sentences. Specifically, the framework employs the pre-trained full CLIP model as a dual encoder for the images and texts , taking advantage of prior cross-modal alignment knowledge. The Text-guided Image Restoration auxiliary task is proposed with the aim of implicitly mapping abstract textual entities to specific image regions, facilitating alignment between textual and visual embeddings. Additionally, we introduce a cross-modal triplet loss tailored for handling hard samples, enhancing the model's ability to distinguish minor differences. To focus the model on the key components within sentences, we propose a novel text data augmentation technique. Our proposed methods achieve state-of-the-art results on three popular benchmark datasets, and the source code will be made publicly available shortly.

Related papers

• You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval [120.49126407479717]
  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.
  arXiv  Detail & Related papers  (2024-03-12T00:27:18Z)
• Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning [13.357749288588039]
  Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space.
  arXiv  Detail & Related papers  (2023-12-14T12:39:29Z)
• Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training [33.78990448307792]
  Image-text retrieval is a central problem for understanding the semantic relationship between vision and language. Previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework.
  arXiv  Detail & Related papers  (2023-06-15T00:19:13Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search [61.24539128142504]
  Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. Most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities. We propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels.
  arXiv  Detail & Related papers  (2022-08-30T16:14:18Z)
• 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)
• 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)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• Text-based Person Search in Full Images via Semantic-Driven Proposal Generation [42.25611020956918]
  We propose a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals.
  arXiv  Detail & Related papers  (2021-09-27T11:42:40Z)
• 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)
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis [80.54273334640285]
  We propose a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators. We also propose a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images.
  arXiv  Detail & Related papers  (2020-08-13T12:51:17Z)

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.