Learning Hyperspectral Images with Curated Text Prompts for Efficient Multimodal Alignment

• URL: http://arxiv.org/abs/2509.22697v1
• Date: Sat, 20 Sep 2025 23:23:04 GMT
• Title: Learning Hyperspectral Images with Curated Text Prompts for Efficient Multimodal Alignment
• Authors: Abhiroop Chatterjee, Susmita Ghosh,
• Abstract summary: We make an attempt to optimize a Vision-Language Model (VLM) for hyperspectral scene understanding by exploiting a CLIP-style contrastive training framework.<n>Our framework maps voxel-level embeddings from a vision backbone onto the latent space of a frozen large embedding model.<n>It is seen that the proposed method updates only 0.07 percent of the total parameters, yet yields state-of-the-art performance.
• Score: 1.7188280334580195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As data requirements continue to grow, efficient learning increasingly depends on the curation and distillation of high-value data rather than brute-force scaling of model sizes. In the case of a hyperspectral image (HSI), the challenge is amplified by the high-dimensional 3D voxel structure, where each spatial location is associated with hundreds of contiguous spectral channels. While vision and language models have been optimized effectively for natural image or text tasks, their cross-modal alignment in the hyperspectral domain remains an open and underexplored problem. In this article, we make an attempt to optimize a Vision-Language Model (VLM) for hyperspectral scene understanding by exploiting a CLIP-style contrastive training framework. Our framework maps voxel-level embeddings from a vision backbone onto the latent space of a frozen large embedding model (LEM), where a trainable probe aligns vision features with the model's textual token representations. The two modalities are aligned via a contrastive loss restricted to a curated set of hard (closest wrong classes) and semi-hard (random distractors) negatives, along with positive pairs. To further enhance alignment, descriptive prompts that encode class semantics are introduced and act as structured anchors for the HSI embeddings. It is seen that the proposed method updates only 0.07 percent of the total parameters, yet yields state-of-the-art performance. For example, on Indian Pines (IP) the model produces better results over unimodal and multimodal baselines by +0.92 Overall Accuracy (OA) and +1.60 Kappa ($\kappa$), while on Pavia University (PU) data it provides gains of +0.69 OA and +0.90 $\kappa$. Moreover, this is achieved with the set of parameters, nearly 50$\times$ smaller than DCTN and 90$\times$ smaller than SS-TMNet.

Related papers

• Efficient Learning of Sparse Representations from Interactions [9.381985901356922]
  We propose a training strategy for learning high-dimensional sparse embedding layers in place of conventional dense ones.<n>We modified the production-grade collaborative filtering autoencoder ELSA, achieving up to 10x reduction in embedding size with no loss of recommendation accuracy.
  arXiv  Detail & Related papers  (2026-02-10T16:09:58Z)
• Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models [84.78794648147608]
  A persistent geometric anomaly, the Modality Gap, remains.<n>Prior approaches to bridge this gap are largely limited by oversimplified isotropic assumptions.<n>We propose the Fixed-frame Modality Gap Theory, which decomposes the modality gap into stable biases and anisotropic residuals.<n>We then introduce ReAlign, a training-free modality alignment strategy.
  arXiv  Detail & Related papers  (2026-02-02T13:59:39Z)
• $β$-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment [53.42377319350806]
  $$-CLIP is a multi-granular text-conditioned contrastive learning framework.<n>$$-CAL addresses the semantic overlap inherent in this hierarchy.<n>$$-CLIP establishes a robust, adaptive baseline for dense vision-language correspondence.
  arXiv  Detail & Related papers  (2025-12-14T13:03:20Z)
• DReX: Pure Vision Fusion of Self-Supervised and Convolutional Representations for Image Complexity Prediction [1.771934382051849]
  We propose a vision-only model that fuses self-supervised and convolutional representations to predict image complexity.<n>DReX achieves state-of-the-art performance on the IC9600 benchmark.<n>Our findings suggest that visual features alone can be sufficient for human-aligned complexity prediction.
  arXiv  Detail & Related papers  (2025-11-21T06:57:33Z)
• FG-CLIP: Fine-Grained Visual and Textual Alignment [3.830067625507938]
  We propose Fine-Grained CLIP, which enhances fine-grained understanding through three key innovations.<n>We leverage large multimodal models to generate 1.6 billion long caption-image pairs for capturing global-level semantic details.<n>We construct a comprehensive dataset, termed FineHARD, by integrating high-quality region-specific annotations with hard fine-grained negative samples.
  arXiv  Detail & Related papers  (2025-05-08T09:06:53Z)
• TULIP: Towards Unified Language-Image Pretraining [60.99500935831526]
  We introduce T, an open-source, drop-in replacement for existing CLIP-like models.<n>Our method leverages generative data augmentation, enhanced image-image and text-text contrastive learning, and image/text reconstruction regularization to learn fine-grained visual features.<n>Our approach, scaling to over 1B parameters, outperforms existing state-of-the-art (SOTA) models across benchmarks.
  arXiv  Detail & Related papers  (2025-03-19T17:58:57Z)
• EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models [64.18350535770357]
  We propose an automatic pruning method for large vision-language models to enhance the efficiency of multimodal reasoning.<n>Our approach only leverages a small number of samples to search for the desired pruning policy.<n>We conduct extensive experiments on the ScienceQA, Vizwiz, MM-vet, and LLaVA-Bench datasets for the task of visual question answering.
  arXiv  Detail & Related papers  (2025-03-19T16:07:04Z)
• MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification [19.29480118378639]
  Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels.<n>This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification.
  arXiv  Detail & Related papers  (2025-02-11T09:42:13Z)
• UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks [60.46473247205654]
  Using large-scale unsupervised unimodal models as pre-training can enhance the zero-shot performance of image-text pair models. Our experiments show that unimodal pre-training outperforms state-of-the-art CLIP-based models.
  arXiv  Detail & Related papers  (2023-06-07T18:26:22Z)
• DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition [62.95223898214866]
  We explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks.
  arXiv  Detail & Related papers  (2023-02-03T14:59:31Z)
• Adaptive Context-Aware Multi-Modal Network for Depth Completion [107.15344488719322]
  We propose to adopt the graph propagation to capture the observed spatial contexts. We then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks.
  arXiv  Detail & Related papers  (2020-08-25T06:00:06Z)

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.