CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning

• URL: http://arxiv.org/abs/2507.20411v1
• Date: Sun, 27 Jul 2025 21:00:02 GMT
• Title: CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning
• Authors: George Ibrahim, Rita Ramos, Yova Kementchedjhieva,
• Abstract summary: We introduce CONCAP, a multilingual image captioning model that integrates retrieved captions with image-specific concepts.<n>Experiments on the XM3600 dataset indicate that CONCAP enables strong performance on low- and mid-resource languages.
• Score: 7.439550425786999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual vision-language models have made significant strides in image captioning, yet they still lag behind their English counterparts due to limited multilingual training data and costly large-scale model parameterization. Retrieval-augmented generation (RAG) offers a promising alternative by conditioning caption generation on retrieved examples in the target language, reducing the need for extensive multilingual training. However, multilingual RAG captioning models often depend on retrieved captions translated from English, which can introduce mismatches and linguistic biases relative to the source language. We introduce CONCAP, a multilingual image captioning model that integrates retrieved captions with image-specific concepts, enhancing the contextualization of the input image and grounding the captioning process across different languages. Experiments on the XM3600 dataset indicate that CONCAP enables strong performance on low- and mid-resource languages, with highly reduced data requirements. Our findings highlight the effectiveness of concept-aware retrieval augmentation in bridging multilingual performance gaps.

Related papers

• Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularization [9.349707150988893]
  We propose a continuous multilingual integration strategy that injects text-only multilingual data during visual instruction tuning.<n>Our approach significantly improves linguistic fidelity across languages without degradation in visual performance.
  arXiv  Detail & Related papers  (2025-03-28T16:26:52Z)
• Towards Retrieval-Augmented Architectures for Image Captioning [81.11529834508424]
  This work presents a novel approach towards developing image captioning models that utilize an external kNN memory to improve the generation process. Specifically, we propose two model variants that incorporate a knowledge retriever component that is based on visual similarities. We experimentally validate our approach on COCO and nocaps datasets and demonstrate that incorporating an explicit external memory can significantly enhance the quality of captions.
  arXiv  Detail & Related papers  (2024-05-21T18:02:07Z)
• LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting [15.266569206458648]
  We propose LMCap, an image-blind few-shot multilingual captioning model that works by prompting a language model with retrieved captions. Experiments on the XM3600 dataset of geographically diverse images show that our model is competitive with fully-supervised multilingual captioning models.
  arXiv  Detail & Related papers  (2023-05-31T13:03:17Z)
• M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval [56.49878599920353]
  This work investigates the use of large-scale, English-only pre-trained models (CLIP and HuBERT) for multilingual image-speech retrieval. For non-English image-speech retrieval, we outperform the current state-of-the-art performance by a wide margin both when training separate models for each language, and with a single model which processes speech in all three languages.
  arXiv  Detail & Related papers  (2022-11-02T14:54:45Z)
• Retrieval-Augmented Transformer for Image Captioning [51.79146669195357]
  We develop an image captioning approach with a kNN memory, with which knowledge can be retrieved from an external corpus to aid the generation process. Our architecture combines a knowledge retriever based on visual similarities, a differentiable encoder, and a kNN-augmented attention layer to predict tokens. Experimental results, conducted on the COCO dataset, demonstrate that employing an explicit external memory can aid the generation process and increase caption quality.
  arXiv  Detail & Related papers  (2022-07-26T19:35:49Z)
• Multimodal Knowledge Alignment with Reinforcement Learning [103.68816413817372]
  ESPER extends language-only zero-shot models to unseen multimodal tasks, like image and audio captioning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of zero-shot tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:12:17Z)
• Injecting Semantic Concepts into End-to-End Image Captioning [61.41154537334627]
  We propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features. For improved performance, we introduce a novel Concept Token Network (CTN) to predict the semantic concepts and then incorporate them into the end-to-end captioning. In particular, the CTN is built on the basis of a vision transformer and is designed to predict the concept tokens through a classification task.
  arXiv  Detail & Related papers  (2021-12-09T22:05:05Z)
• UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training [52.852163987208826]
  UC2 is the first machine translation-augmented framework for cross-lingual cross-modal representation learning. We propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM) Our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
  arXiv  Detail & Related papers  (2021-04-01T08:30:53Z)
• Fusion Models for Improved Visual Captioning [18.016295296424413]
  This paper proposes a generic multimodal model fusion framework for caption generation and emendation. We employ the same fusion strategies to integrate a pretrained Masked Language Model (MLM) with a visual captioning model, viz. Show, Attend, and Tell. Our caption emendation experiments on three benchmark image captioning datasets, viz. Flickr8k, Flickr30k, and MSCOCO, show improvements over the baseline.
  arXiv  Detail & Related papers  (2020-10-28T21:55:25Z)
• Improving Image Captioning with Better Use of Captions [65.39641077768488]
  We present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences.
  arXiv  Detail & Related papers  (2020-06-21T14:10:47Z)
• Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage [23.71195344840051]
  Cross-modal language generation tasks such as image captioning are directly hurt by the trend of data-hungry models combined with the lack of non-English annotations. We describe an approach called Pivot-Language Generation Stabilization (PLuGS), which leverages directly at training time both existing English annotations and their machine-translated versions. We show that PLuGS models outperform other candidate solutions in evaluations performed over 5 different target languages.
  arXiv  Detail & Related papers  (2020-05-01T06:58:18Z)

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.