SAViL-Det: Semantic-Aware Vision-Language Model for Multi-Script Text Detection

• URL: http://arxiv.org/abs/2507.20188v1
• Date: Sun, 27 Jul 2025 09:16:39 GMT
• Title: SAViL-Det: Semantic-Aware Vision-Language Model for Multi-Script Text Detection
• Authors: Mohammed-En-Nadhir Zighem, Abdenour Hadid,
• Abstract summary: This paper introduces SAViL-Det, a novel semantic-aware vision-language model that enhances multi-script text detection.<n>The proposed framework adaptively propagates fine-grained semantic information from text prompts to visual features via cross-modal attention.<n>Experiments on challenging benchmarks demonstrate the effectiveness of the proposed approach.
• Score: 4.013156524547072
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting text in natural scenes remains challenging, particularly for diverse scripts and arbitrarily shaped instances where visual cues alone are often insufficient. Existing methods do not fully leverage semantic context. This paper introduces SAViL-Det, a novel semantic-aware vision-language model that enhances multi-script text detection by effectively integrating textual prompts with visual features. SAViL-Det utilizes a pre-trained CLIP model combined with an Asymptotic Feature Pyramid Network (AFPN) for multi-scale visual feature fusion. The core of the proposed framework is a novel language-vision decoder that adaptively propagates fine-grained semantic information from text prompts to visual features via cross-modal attention. Furthermore, a text-to-pixel contrastive learning mechanism explicitly aligns textual and corresponding visual pixel features. Extensive experiments on challenging benchmarks demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance with F-scores of 84.8% on the benchmark multi-lingual MLT-2019 dataset and 90.2% on the curved-text CTW1500 dataset.

Related papers

• Visual Text Processing: A Comprehensive Review and Unified Evaluation [99.57846940547171]
  We present a comprehensive, multi-perspective analysis of recent advancements in visual text processing.<n>Our aim is to establish this work as a fundamental resource that fosters future exploration and innovation in the dynamic field of visual text processing.
  arXiv  Detail & Related papers  (2025-04-30T14:19:29Z)
• FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction [66.98008357232428]
  We propose FineMatch, a new aspect-based fine-grained text and image matching benchmark. FineMatch focuses on text and image mismatch detection and correction. We show that models trained on FineMatch demonstrate enhanced proficiency in detecting fine-grained text and image mismatches.
  arXiv  Detail & Related papers  (2024-04-23T03:42:14Z)
• 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)
• CLIP-Count: Towards Text-Guided Zero-Shot Object Counting [32.07271723717184]
  We propose CLIP-Count, the first end-to-end pipeline that estimates density maps for open-vocabulary objects with text guidance in a zero-shot manner. To align the text embedding with dense visual features, we introduce a patch-text contrastive loss that guides the model to learn informative patch-level visual representations for dense prediction. Our method effectively generates high-quality density maps for objects-of-interest.
  arXiv  Detail & Related papers  (2023-05-12T08:19:39Z)
• Learning the Visualness of Text Using Large Vision-Language Models [42.75864384249245]
  Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. We curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators.
  arXiv  Detail & Related papers  (2023-05-11T17:45:16Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• 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)

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.