Infusing fine-grained visual knowledge to Vision-Language Models

• URL: http://arxiv.org/abs/2508.12137v1
• Date: Sat, 16 Aug 2025 19:12:09 GMT
• Title: Infusing fine-grained visual knowledge to Vision-Language Models
• Authors: Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araujo, Ondřej Chum,
• Abstract summary: Large-scale contrastive pre-training produces powerful Vision-and-Language Models (VLMs)<n>We propose a fine-tuning method explicitly designed to achieve optimal balance between fine-grained domain adaptation and retention of the pretrained VLM's broad multimodal knowledge.<n>Our approach consistently achieves strong results, notably retaining the visual-text alignment without utilizing any text data or the original text encoder during fine-tuning.
• Score: 5.487134463783365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale contrastive pre-training produces powerful Vision-and-Language Models (VLMs) capable of generating representations (embeddings) effective for a wide variety of visual and multimodal tasks. However, these pretrained embeddings remain suboptimal for fine-grained open-set visual retrieval, where state-of-the-art results require fine-tuning the vision encoder using annotated domain-specific samples. Naively performing such fine-tuning typically leads to catastrophic forgetting, severely diminishing the model's general-purpose visual and cross-modal capabilities. In this work, we propose a fine-tuning method explicitly designed to achieve optimal balance between fine-grained domain adaptation and retention of the pretrained VLM's broad multimodal knowledge. Drawing inspiration from continual learning literature, we systematically analyze standard regularization techniques aimed at knowledge retention and propose an efficient and effective combination strategy. Additionally, we address the commonly overlooked yet critical aspects of validation set design and hyperparameter tuning to ensure reproducibility and robust generalization across datasets and pretrained models. We extensively evaluate our method on both fine-grained and coarse-grained image-image and image-text retrieval benchmarks. Our approach consistently achieves strong results, notably retaining the visual-text alignment without utilizing any text data or the original text encoder during fine-tuning. Code and model checkpoints: https://github.com/nikosips/infusing .

Related papers

• Unleashing the Power of Vision-Language Models for Long-Tailed Multi-Label Visual Recognition [55.189113121465816]
  We propose a novel correlation adaptation prompt network (CAPNET) for long-tailed multi-label visual recognition.<n>CAPNET explicitly models correlations from CLIP's textual encoder.<n>It improves generalization through test-time ensembling and realigns visual-textual modalities.
  arXiv  Detail & Related papers  (2025-11-25T18:57:28Z)
• Decouple before Align: Visual Disentanglement Enhances Prompt Tuning [85.91474962071452]
  Prompt tuning (PT) has showcased remarkable effectiveness in improving the task-specific transferability of vision-language models.<n>This paper delves into a previously overlooked information asymmetry issue in PT, where the visual modality mostly conveys more context.<n>We propose DAPT, an effective PT framework based on an intuitive decouple-before-align concept.
  arXiv  Detail & Related papers  (2025-08-01T07:46:00Z)
• Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition [38.74388860692423]
  We propose a novel approach, Semantic-guided fine-tuning of foundation model for long-tailed visual recognition (Sage)<n>We introduce an SG-Adapter that integrates class descriptions as semantic guidance to guide the fine-tuning of the visual encoder.<n>Experiments on benchmark datasets demonstrate the effectiveness of the proposed Sage in enhancing performance in long-tailed learning.
  arXiv  Detail & Related papers  (2025-07-17T05:47:19Z)
• Top-Down Compression: Revisit Efficient Vision Token Projection for Visual Instruction Tuning [70.57180215148125]
  Visual instruction tuning aims to enable large language models to comprehend the visual world.<n>Existing methods often grapple with the intractable trade-off between accuracy and efficiency.<n>We present LLaVA-Meteor, a novel approach that strategically compresses visual tokens without compromising core information.
  arXiv  Detail & Related papers  (2025-05-17T10:22:29Z)
• Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization [40.77611907215627]
  Large Vision Language Models (VLMs) are prone to significant hallucinations, particularly in the form of cross-modal inconsistencies.<n>We introduce Re-Align, a novel alignment framework that leverages image retrieval to construct a dual-preference dataset.<n>We also introduce rDPO, an extension of the standard direct preference optimization that incorporates an additional visual preference objective during fine-tuning.
  arXiv  Detail & Related papers  (2025-02-18T18:59:57Z)
• Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment [57.0121616203175]
  We propose FiSAO, a novel self-alignment method that utilizes the model's own visual encoder as a fine-grained verifier to improve vision-language alignment.<n>By leveraging token-level feedback from the vision encoder, FiSAO significantly improves vision-language alignment, even surpassing traditional preference tuning methods that require additional data.
  arXiv  Detail & Related papers  (2024-10-18T03:34:32Z)
• Calibrated Self-Rewarding Vision Language Models [27.686545023186852]
  Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. LVLMs often exhibit the hallucination phenomenon, where generated text responses appear linguistically plausible but contradict the input image. We propose the Calibrated Self-Rewarding (CSR) approach, which enables the model to self-improve by iteratively generating candidate responses, evaluating the reward for each response, and curating preference data for fine-tuning.
  arXiv  Detail & Related papers  (2024-05-23T14:30:33Z)
• Vision-Enhanced Semantic Entity Recognition in Document Images via Visually-Asymmetric Consistency Learning [19.28860833813788]
  Existing models commonly train a visual encoder with weak cross-modal supervision signals. We propose a novel textbfVisually-textbfAsymmetric cotextbfNsistentextbfCy textbfLearning (textscVancl) approach to capture fine-grained visual and layout features.
  arXiv  Detail & Related papers  (2023-10-23T10:37:22Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models [65.19308052012858]
  Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. We present VALUE, a set of meticulously designed probing tasks to decipher the inner workings of multimodal pre-training. Key observations: Pre-trained models exhibit a propensity for attending over text rather than images during inference.
  arXiv  Detail & Related papers  (2020-05-15T01:06:54Z)

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.