Guiding Medical Vision-Language Models with Diverse Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations (2025.naacl-long)
Copied to clipboard
| Challenge: | Current vision-language models lack the ability to focus on specific areas designated by humans . a new framework that integrates medical entity extraction, visual prompt generation, and dataset adaptation is proposed to improve visual prompt-guided fine-tuning. |
| Approach: | They propose to use visual prompts to guide and enhance formation of region-specific attention. |
| Outcome: | The proposed framework outperforms state-of-the-art large vision-language models on medical datasets. |
Similar Papers
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Pretrained biomedical vision–language models perform well on average but often degrade on challenging modalities. |
| Approach: | They propose a prompt-learning framework that improves cross-domain generalization without extensive backbone fine-tuning. |
| Outcome: | BioVLM learns a diverse prompt bank and introduces dynamic prompt selection . it can combine sparse few-shot evidence with rich LLM semantic priors . bioVLM achieves state-of-the-art on 11 MedMNIST+ 2D datasets based on the proposed framework . |
Benchmarking and Mitigating the Impact of Noisy User Prompts in Medical VLMs via Cross-Modal Reflection (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing medical vision-language models follow user-provided prompts blindly, a new study finds . current models are noisy, causing problems with reliability in real-world interactions . |
| Approach: | They propose a method to evaluate the influence of clinical prompts on medical vision-language models . they use cross-modal reflection chain-of-thought to train the model to produce reasoning paths . |
| Outcome: | The proposed method significantly improves the robustness against noisy prompts . existing Med-VLMs follow user-provided prompts blindly, the authors show . |
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. |
| Approach: | They propose a framework for automatic attention alignment tuning that leverages weak labels from SAM and selectively modifies visually-critical attention heads to improve alignment while minimizing interference. |
| Outcome: | The proposed framework outperforms state-of-the-art models on medical VQA and report generation benchmarks. |
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for rewriting text-to-image models require specialized vocabulary . a new approach uses large vision language models to optimize text-based models . |
| Approach: | They propose a prompt optimization framework that rephrases a user prompt into a text-to-image model by using large vision language models as solver and reward model. |
| Outcome: | The proposed model outperforms existing models on two popular datasets. |
Visual Prompting in LLMs for Enhancing Emotion Recognition (2024.emnlp-main)
Copied to clipboard
Qixuan Zhang, Zhifeng Wang, Dylan Zhang, Wenjia Niu, Sabrina Caldwell, Tom Gedeon, Yang Liu, Zhenyue Qin
| Challenge: | Existing methods for enhancing in-context emotion classification fail to include spatial relationships between different people and facial features within a single face. |
| Approach: | They propose a set-of-vision prompting approach that uses spatial information to mark targets precisely. |
| Outcome: | The proposed approach improves face count and emotion categorization while preserving the enriched image context. |
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Several studies claim that domain-adaptive pretraining improves performance on downstream medical tasks. |
| Approach: | They compare medical LLMs and VLMs against their corresponding base models . they find that medical Lms outperform their base models in 12.1% of cases . |
| Outcome: | The proposed models outperform their base models on medical questions and tasks in 12.1% of cases and reach a tie in 49.8% of cases. |
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Recent few-shot learning models such as GPT3 are expensive and slow to deploy for real-world applications. |
| Approach: | They propose a prompt-based low-resource learning method for VL tasks with a few examples . they pre-train a sequence-to-sequence transformer model with prefix and masked language modeling . |
| Outcome: | The proposed method outperforms Frozen on vision-language tasks with prompt-based learning by 18.2% point. |
EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data. |
| Approach: | They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. |
| Outcome: | The proposed framework shows consistent performance improvements on four benchmarks. |
Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents (2025.emnlp-main)
Copied to clipboard
| Challenge: | Visual Language Models (VLMs) have shown strong performance in tasks like radiology report generation but struggle with hallucinations, vague descriptions, Inconsistent logic and poor localization. |
| Approach: | They propose a framework for medical visual reasoning based on Visual Guidance and Self-Reward paradigms and Monte Carlo Tree Search to improve the model's visual reasoning capabilities. |
| Outcome: | The proposed framework outperforms existing models on multiple medical VQA benchmarks. |
LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained vision-language models (VLMs) often suffer from bias in class-imbalanced scenes. |
| Approach: | They propose a multi-dimensional dynamic prompt routing framework that integrates a knowledge base for classes spanning multiple visual-semantic dimensions. |
| Outcome: | The proposed framework achieves comparable results with current SOTA methods on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT and Places-LT. |