Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning

• URL: http://arxiv.org/abs/2609.05636v1
• Date: Fri, 04 Sep 2026 18:14:44 GMT
• Title: Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning
• Abstract summary: Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations.<n>Each new task still demands handcrafted prompts.<n>Active Prompt Learning (APL) combines Active Learning (AL) and Prompt Learning (PL) into a single framework.
• Score: 1.5539424546888112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but each new task still demands handcrafted prompts. Active Prompt Learning (APL) combines Active Learning (AL) and Prompt Learning (PL) into a single framework, allowing for the usage of the VLM prior knowledge for iteratively querying the most informative images to be labeled. However, the cold-start problem is still relevant for APL methods, where the performance of the initial query can be worse than random sampling. While recent state-of-the-art APL methods mitigate with balanced sampling and multimodal features, they rely on rigid, distance-based clustering to group these features. This simplistic approach can struggle to capture the complex, high-dimensional semantic distributions inherent to VLMs, leading to suboptimal query representativeness. Unsupervised transfer can be applied to these features as a possible alternative, since it is capable of inferring the underlying human labeling of a task without any form of supervision. This way, samples can be grouped in semantically coherent clusters. This paper proposes Unsupervised Transfer Clustering with Selective Querying (UTC+SQ), a framework that enhances a recent APL approach by leveraging state-of-the-art unsupervised transfer model. These models generate high-fidelity pseudo-labels that establish semantically meaningful clusters, allowing for the selection of more relevant samples. Experimental evaluations demonstrate that shifting from distance-based to projection-based clustering improves the representativeness of the queried subset, achieving accuracy gains in 6 of the 8 datasets tested.

Related papers

• From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space [78.36537400975298]
  Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models.<n>We propose Multi-View GRPO, a novel approach that enhances relationship exploration by augmenting the condition space.<n>MV-GRPO achieves superior alignment performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-13T04:35:13Z)
• GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering [9.33534873952683]
  This paper introduces GALACTIC, the first unified framework to bridge local and global counterfactual explainability for unsupervised time-series clustering.<n>At instance level (local), GALACTIC generates perturbations via a cluster-aware optimization objective.<n>At cluster level (global), to mitigate cognitive load and enhance interpretability, we formulate a representative CE selection problem.
  arXiv  Detail & Related papers  (2026-03-05T15:59:33Z)
• ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering [79.69917150582633]
  Multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering.<n>Our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features.<n>We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy.
  arXiv  Detail & Related papers  (2025-11-30T04:36:51Z)
• Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning [3.4530027457862005]
  We introduce a novel clustering approach based on meta-learning.<n>We employ a pre-trained Prior-Data Fitted Transformer Network (PFN) to perform clustering.<n>We show that our approach is superior to the state-of-the-art clustering techniques.
  arXiv  Detail & Related papers  (2025-07-27T17:53:19Z)
• Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation [21.20806568508201]
  We show how to leverage class text information to mitigate distribution drifts encountered by vision-language models (VLMs) during test-time inference.<n>We propose to generate pseudo-labels for the test-time samples by exploiting generic class text embeddings as fixed centroids of a label assignment problem.<n>Experiments on multiple popular test-time adaptation benchmarks presenting diverse complexity empirically show the superiority of CLIP-OT.
  arXiv  Detail & Related papers  (2024-11-26T00:15:37Z)
• In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement [71.60563181678323]
  Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality.<n>To handle these challenges, a direct solution is to generate high-confidence'' data from unsupervised downstream tasks.<n>We propose a novel approach, pseudo-supervised demonstrations aligned prompt optimization (PAPO) algorithm, which jointly refines both the prompt and the overall pseudo-supervision.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances [24.142013877384603]
  This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training. We show remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain.
  arXiv  Detail & Related papers  (2024-05-21T13:24:07Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Generalized Category Discovery with Large Language Models in the Loop [10.440661581492723]
  We propose Loop, an end-to-end active-learning framework that introduces Large Language Models into the training loop. We show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters.
  arXiv  Detail & Related papers  (2023-12-18T02:55:14Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• Progressive Cluster Purification for Unsupervised Feature Learning [48.87365358296371]
  In unsupervised feature learning, sample specificity based methods ignore the inter-class information. We propose a novel clustering based method, which excludes class inconsistent samples during progressive cluster formation. Our approach, referred to as Progressive Cluster Purification (PCP), implements progressive clustering by gradually reducing the number of clusters during training.
  arXiv  Detail & Related papers  (2020-07-06T08:11:03Z)

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.