Ranking-aware adapter for text-driven image ordering with CLIP

• URL: http://arxiv.org/abs/2412.06760v3
• Date: Sat, 08 Feb 2025 03:25:39 GMT
• Title: Ranking-aware adapter for text-driven image ordering with CLIP
• Authors: Wei-Hsiang Yu, Yen-Yu Lin, Ming-Hsuan Yang, Yi-Hsuan Tsai,
• Abstract summary: We propose an effective yet efficient approach that reframes the CLIP model into a learning-to-rank task. Our approach incorporates learnable prompts to adapt to new instructions for ranking purposes. Our ranking-aware adapter consistently outperforms fine-tuned CLIPs on various tasks.
• Score: 76.80965830448781
• License:
• Abstract: Recent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore applications like image ranking and retrieval. However, existing studies typically focus on the reasoning based on a single image and heavily depend on text prompting, limiting their ability to learn comprehensive understanding from multiple images. To address this, we propose an effective yet efficient approach that reframes the CLIP model into a learning-to-rank task and introduces a lightweight adapter to augment CLIP for text-guided image ranking. Specifically, our approach incorporates learnable prompts to adapt to new instructions for ranking purposes and an auxiliary branch with ranking-aware attention, leveraging text-conditioned visual differences for additional supervision in image ranking. Our ranking-aware adapter consistently outperforms fine-tuned CLIPs on various tasks and achieves competitive results compared to state-of-the-art models designed for specific tasks like facial age estimation and image quality assessment. Overall, our approach primarily focuses on ranking images with a single instruction, which provides a natural and generalized way of learning from visual differences across images, bypassing the need for extensive text prompts tailored to individual tasks. Code is available: github.com/uynaes/RankingAwareCLIP.

Related papers

• CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP [19.80268944768578]
  Blind dehazed image quality assessment (BDQA) aims to accurately predict the visual quality of dehazed images without any reference information. We propose to adapt Contrastive Language-Image Pre-Training (CLIP), pre-trained on large-scale image-text pairs, to the BDQA task. We show that our proposed approach, named CLIP-DQA, achieves more accurate quality predictions over existing BDQA methods.
  arXiv  Detail & Related papers  (2025-02-03T14:12:25Z)
• Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training [30.071860810401933]
  This paper advances contrastive language-image pre-training (CLIP) into one novel holistic paradigm. We use image-to-text captioning to generate multi-texts for each image, from multiple perspectives, granularities, and hierarchies. Our holistic CLIP significantly outperforms existing CLIP, including image-text retrieval, open-vocabulary classification, and dense visual tasks.
  arXiv  Detail & Related papers  (2024-11-30T11:27:58Z)
• Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment [57.07360640784803]
  We propose vision-language consistency guided multi-modal prompt learning for blind image quality assessment (AGIQA) Specifically, we introduce learnable textual and visual prompts in language and vision branches of Contrastive Language-Image Pre-training (CLIP) models. We design a text-to-image alignment quality prediction task, whose learned vision-language consistency knowledge is used to guide the optimization of the above multi-modal prompts.
  arXiv  Detail & Related papers  (2024-06-24T13:45:31Z)
• Enhancing Image Retrieval : A Comprehensive Study on Photo Search using the CLIP Mode [0.27195102129095]
  Photo search has witnessed significant advancements with the introduction of CLIP (Contrastive Language-Image Pretraining) model. This abstract summarizes the foundational principles of CLIP and highlights its potential impact on advancing the field of photo search.
  arXiv  Detail & Related papers  (2024-01-24T17:35:38Z)
• CLIP Guided Image-perceptive Prompt Learning for Image Enhancement [15.40368082025006]
  Contrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning is proposed. We learn image-perceptive prompts to distinguish between original and target images using CLIP model. We introduce a very simple network by incorporating a simple baseline to predict the weights of three different LUT as enhancement network.
  arXiv  Detail & Related papers  (2023-11-07T12:36:20Z)
• Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features [32.138956674478116]
  Given a query composed of a reference image and a relative caption, the Composed Image Retrieval goal is to retrieve images visually similar to the reference one. We use features from the OpenAI CLIP model to tackle the considered task. We train a Combiner network that learns to combine the image-text features integrating the bimodal information.
  arXiv  Detail & Related papers  (2023-08-22T15:03:16Z)
• Iterative Prompt Learning for Unsupervised Backlit Image Enhancement [86.90993077000789]
  We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT. We show that the open-world CLIP prior aids in distinguishing between backlit and well-lit images. Our method alternates between updating the prompt learning framework and enhancement network until visually pleasing results are achieved.
  arXiv  Detail & Related papers  (2023-03-30T17:37:14Z)
• SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification [84.05253637260743]
  We propose a new framework, named Semantic-guided Visual Adapting (SgVA), to extend vision-language pre-trained models. SgVA produces discriminative task-specific visual features by comprehensively using a vision-specific contrastive loss, a cross-modal contrastive loss, and an implicit knowledge distillation. State-of-the-art results on 13 datasets demonstrate that the adapted visual features can well complement the cross-modal features to improve few-shot image classification.
  arXiv  Detail & Related papers  (2022-11-28T14:58:15Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)
• CLIP-Adapter: Better Vision-Language Models with Feature Adapters [79.52844563138493]
  We show that there is an alternative path to achieve better vision-language models other than prompt tuning. In this paper, we propose CLIP-Adapter to conduct fine-tuning with feature adapters on either visual or language branch. Experiments and extensive ablation studies on various visual classification tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-10-09T11:39:30Z)

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.