Semi-Supervised Few-Shot Adaptation of Vision-Language Models

• URL: http://arxiv.org/abs/2603.02959v1
• Date: Tue, 03 Mar 2026 13:11:47 GMT
• Title: Semi-Supervised Few-Shot Adaptation of Vision-Language Models
• Authors: Julio Silva-Rodríguez, Ender Konukoglu,
• Abstract summary: In medical imaging, specialized vision-supervised models (VLMs) have shown promising performance in zero- and few-shot image classification.<n>We propose leveraging unlabeled data by introducing an efficient semi-language solver that propagates text-informed pseudo-labels during few-shot adaptation.
• Score: 20.999372254003482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) pre-trained on large, heterogeneous data sources are becoming increasingly popular, providing rich multi-modal embeddings that enable efficient transfer to new tasks. A particularly relevant application is few-shot adaptation, where only a handful of annotated examples are available to adapt the model through multi-modal linear probes. In medical imaging, specialized VLMs have shown promising performance in zero- and few-shot image classification, which is valuable for mitigating the high cost of expert annotations. However, challenges remain in extremely low-shot regimes: the inherent class imbalances in medical tasks often lead to underrepresented categories, penalizing overall model performance. To address this limitation, we propose leveraging unlabeled data by introducing an efficient semi-supervised solver that propagates text-informed pseudo-labels during few-shot adaptation. The proposed method enables lower-budget annotation pipelines for adapting VLMs, reducing labeling effort by >50% in low-shot regimes.

Related papers

• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Steering and Rectifying Latent Representation Manifolds in Frozen Multi-modal LLMs for Video Anomaly Detection [52.5174167737992]
  Video anomaly detection (VAD) aims to identify abnormal events in videos.<n>We propose SteerVAD, which advances MLLM-based VAD by shifting from passively reading to actively steering and rectifying internal representations.<n>Our method achieves state-of-the-art performance among tuning-free approaches requiring only 1% of training data.
  arXiv  Detail & Related papers  (2026-02-27T13:48:50Z)
• WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens [69.97021957331326]
  We propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization.<n>We also introduce a VAE branch with linear projection to recover fine-grained image details.
  arXiv  Detail & Related papers  (2025-12-02T09:02:20Z)
• 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)
• Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts [13.21626568246313]
  We analyze whether vision-language foundation models can be adapted to target datasets with very different distributions and classes.<n>We propose a novel prompt-tuning method, PromptMargin, for adapting such large-scale VLMs directly on the few target samples.<n>PromptMargin effectively tunes the text as well as visual prompts for this task, and has two main modules.
  arXiv  Detail & Related papers  (2025-05-21T13:26:56Z)
• Generalizable Vision-Language Few-Shot Adaptation with Predictive Prompts and Negative Learning [1.3680468021400563]
  Few-shot adaptation remains a core challenge for vision-language models (VLMs)<n>We propose PromptFuseNL, a unified framework that enhances few-shot generalization by combining predictive prompt tuning with dual-branch positive and negative learning.
  arXiv  Detail & Related papers  (2025-05-16T23:39:34Z)
• Feeding LLM Annotations to BERT Classifiers at Your Own Risk [14.533304890042361]
  Using LLM-generated labels to fine-tune smaller encoder-only models for text classification has gained popularity in various settings.<n>We demonstrate how the perennial curse of training on synthetic data manifests itself in this specific setup.<n>Compared to models trained on gold labels, we observe not only the expected performance degradation in accuracy and F1 score, but also increased instability across training runs and premature performance plateaus.
  arXiv  Detail & Related papers  (2025-04-21T20:54:55Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• Low-Rank Few-Shot Adaptation of Vision-Language Models [13.803180972839213]
  We introduce Low-Rank Adaptation (LoRA) in few-shot learning for Vision-Language Models (VLMs) Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times. Our results do not dismiss the potential of prompt-learning and adapter-based research.
  arXiv  Detail & Related papers  (2024-05-28T19:16:59Z)
• Enhancing Vision-Language Few-Shot Adaptation with Negative Learning [11.545127156146368]
  We propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit task-specific knowledge. To this issue, we introduce a plug-and-play few-shot instance reweighting technique to mitigate noisy outliers. Our extensive experimental results validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks.
  arXiv  Detail & Related papers  (2024-03-19T17:59:39Z)
• Debiasing Multimodal Large Language Models via Penalization of Language Priors [38.97645845493758]
  Multimodal Large Language Models (MLLMs) have become indispensable tools in computer vision and natural language processing.<n>Despite their advancements, our investigation reveals a noteworthy bias: the generated content is often driven more by the inherent priors of the underlying Large Language Models (LLMs) than by the input image.<n>We propose two simple, training-free strategies to rectify these biases and redirect the model's focus toward visual information.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment [52.489874804051304]
  VoLTA is a new vision-language pre-training paradigm that only utilizes image-caption data but fine-grained region-level image understanding. VoLTA pushes multi-modal fusion deep into the uni-modal backbones during pre-training. Experiments on a wide range of vision- and vision-language downstream tasks demonstrate the effectiveness of VoLTA.
  arXiv  Detail & Related papers  (2022-10-09T01:49:58Z)

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.