DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding

• URL: http://arxiv.org/abs/2412.01115v1
• Date: Mon, 02 Dec 2024 04:39:17 GMT
• Title: DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding
• Authors: Hao Wu, Zhihang Zhong, Xiao Sun,
• Abstract summary: Dive Into Retrieval (DIR) is designed to enhance both the image-to-text retrieval process and the utilization of retrieved text. DIR not only maintains competitive in-domain performance but also significantly improves out-of-domain generalization, all without increasing inference costs.
• Score: 10.347788969721844
• License:
• Abstract: Image captioning models often suffer from performance degradation when applied to novel datasets, as they are typically trained on domain-specific data. To enhance generalization in out-of-domain scenarios, retrieval-augmented approaches have garnered increasing attention. However, current methods face two key challenges: (1) image features used for retrieval are often optimized based on ground-truth (GT) captions, which represent the image from a specific perspective and are influenced by annotator biases, and (2) they underutilize the full potential of retrieved text, typically relying on raw captions or parsed objects, which fail to capture the full semantic richness of the data. In this paper, we propose Dive Into Retrieval (DIR), a method designed to enhance both the image-to-text retrieval process and the utilization of retrieved text to achieve a more comprehensive understanding of the visual content. Our approach introduces two key innovations: (1) diffusion-guided retrieval enhancement, where a pretrained diffusion model guides image feature learning by reconstructing noisy images, allowing the model to capture more comprehensive and fine-grained visual information beyond standard annotated captions; and (2) a high-quality retrieval database, which provides comprehensive semantic information to enhance caption generation, especially in out-of-domain scenarios. Extensive experiments demonstrate that DIR not only maintains competitive in-domain performance but also significantly improves out-of-domain generalization, all without increasing inference costs.

Related papers

• CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification [42.429118831928214]
  We explore the use of CLIP (Contrastive Language-Image Pretraining), a vision-language model pretrained on large-scale image-text pairs to align visual and textual features. The adaptation of CLIP to the task presents two primary challenges: learning more fine-grained features to enhance discriminative ability, and learning more domain-invariant features to improve the model's generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-27T14:08:25Z)
• Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration [47.942948541067544]
  We propose using text as an auxiliary invariant representation to reactivate the generative capabilities of diffusion-based restoration models. We introduce Res-Captioner, a module that generates enhanced textual descriptions tailored to image content and degradation levels. We present RealIR, a new benchmark designed to capture diverse real-world scenarios.
  arXiv  Detail & Related papers  (2024-12-01T16:36:22Z)
• 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)
• 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)
• Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning [35.47078178526536]
  Recent advancements in pre-trained large-scale language-image models have ushered in a new era of visual comprehension. This paper tackles two well-known issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of potential data biases within them; (2) the evaluation of image captions and steering of their generation process.
  arXiv  Detail & Related papers  (2023-11-02T06:21:35Z)
• Semantic-Preserving Augmentation for Robust Image-Text Retrieval [27.2916415148638]
  RVSE consists of novel image-based and text-based augmentation techniques called semantic preserving augmentation for image (SPAugI) and text (SPAugT) Since SPAugI and SPAugT change the original data in a way that its semantic information is preserved, we enforce the feature extractors to generate semantic aware embedding vectors. From extensive experiments using benchmark datasets, we show that RVSE outperforms conventional retrieval schemes in terms of image-text retrieval performance.
  arXiv  Detail & Related papers  (2023-03-10T03:50:44Z)
• Re-Imagen: Retrieval-Augmented Text-to-Image Generator [58.60472701831404]
  Retrieval-Augmented Text-to-Image Generator (Re-Imagen) Retrieval-Augmented Text-to-Image Generator (Re-Imagen)
  arXiv  Detail & Related papers  (2022-09-29T00:57:28Z)
• DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis [55.788772366325105]
  We propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed.
  arXiv  Detail & Related papers  (2021-08-27T07:20:34Z)
• Understanding Guided Image Captioning Performance across Domains [22.283016988026926]
  We present a method to control the concepts that an image caption should focus on, using an additional input called the guiding text. Our human-evaluation results indicate that attempting in-the-wild guided image captioning requires access to large, unrestricted-domain training datasets.
  arXiv  Detail & Related papers  (2020-12-04T00:05:02Z)
• 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)

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.