ToPT: Task-Oriented Prompt Tuning for Urban Region Representation Learning

• URL: http://arxiv.org/abs/2602.01610v1
• Date: Mon, 02 Feb 2026 03:56:05 GMT
• Title: ToPT: Task-Oriented Prompt Tuning for Urban Region Representation Learning
• Authors: Zitao Guo, Changyang Jiang, Tianhong Zhao, Jinzhou Cao, Genan Dai, Bowen Zhang,
• Abstract summary: ToPT consists of two modules: spatial-aware region embedding learning (SREL) and task-aware prompting for region embeddings (Prompt4RE)<n>Prompt4RE performs task-oriented prompting: a frozen multimodal large language model (MLLM) processes task-specific templates to obtain semantic vectors.<n>Experiments across multiple tasks and cities show state-of-the-art performance, with improvements of up to 64.2%.
• Score: 6.574674801344639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning effective region embeddings from heterogeneous urban data underpins key urban computing tasks (e.g., crime prediction, resource allocation). However, prevailing two-stage methods yield task-agnostic representations, decoupling them from downstream objectives. Recent prompt-based approaches attempt to fix this but introduce two challenges: they often lack explicit spatial priors, causing spatially incoherent inter-region modeling, and they lack robust mechanisms for explicit task-semantic alignment. We propose ToPT, a two-stage framework that delivers spatially consistent fusion and explicit task alignment. ToPT consists of two modules: spatial-aware region embedding learning (SREL) and task-aware prompting for region embeddings (Prompt4RE). SREL employs a Graphormer-based fusion module that injects spatial priors-distance and regional centrality-as learnable attention biases to capture coherent, interpretable inter-region interactions. Prompt4RE performs task-oriented prompting: a frozen multimodal large language model (MLLM) processes task-specific templates to obtain semantic vectors, which are aligned with region embeddings via multi-head cross-attention for stable task conditioning. Experiments across multiple tasks and cities show state-of-the-art performance, with improvements of up to 64.2\%, validating the necessity and complementarity of spatial priors and prompt-region alignment. The code is available at https://github.com/townSeven/Prompt4RE.git.

Related papers

• EarthMapper: Visual Autoregressive Models for Controllable Bidirectional Satellite-Map Translation [50.433911327489554]
  We introduce EarthMapper, a novel framework for controllable satellite-map translation.<n>We also contribute CNSatMap, a large-scale dataset comprising 302,132 precisely aligned satellite-map pairs across 38 Chinese cities.<n> experiments on CNSatMap and the New York dataset demonstrate EarthMapper's superior performance.
  arXiv  Detail & Related papers  (2025-04-28T02:41:12Z)
• Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport [45.866011150937425]
  We present RAM (Recover And Match), a novel framework that effectively addresses the above issues.<n> RAM achieves state-of-the-art performance on various datasets from three distinct domains.
  arXiv  Detail & Related papers  (2025-03-19T15:33:44Z)
• SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models [68.13636352687257]
  We introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts.
  arXiv  Detail & Related papers  (2024-06-03T17:59:06Z)
• SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation [20.29438820908913]
  Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations. We introduce SCE-MAE, a framework that operates on the vanilla feature map instead of on expensive hypercolumns. We demonstrate through experiments that SCE-MAE is highly effective and robust, outperforming existing SOTA methods by large margins.
  arXiv  Detail & Related papers  (2024-05-28T16:14:10Z)
• Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
  We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion. It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing. Our proposed method performs favorably against other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-05-15T00:17:48Z)
• Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning [50.88504784466931]
  Multi-task dense prediction involves semantic segmentation, depth estimation, and surface normal estimation. Existing solutions typically rely on learning global image representations for global cross-task image matching. Our proposal involves modeling region-wise representations using Gaussian Distributions.
  arXiv  Detail & Related papers  (2024-03-15T12:41:30Z)
• Region-Enhanced Feature Learning for Scene Semantic Segmentation [19.20735517821943]
  We propose using regions as the intermediate representation of point clouds instead of fine-grained points or voxels to reduce the computational burden. We design a region-based feature enhancement (RFE) module, which consists of a Semantic-Spatial Region Extraction stage and a Region Dependency Modeling stage. Our REFL-Net achieves 1.8% mIoU gain on ScanNetV2 and 1.7% mIoU gain on S3DIS datasets with negligible computational cost.
  arXiv  Detail & Related papers  (2023-04-15T06:35:06Z)
• Spatial-Aware Token for Weakly Supervised Object Localization [137.0570026552845]
  We propose a task-specific spatial-aware token to condition localization in a weakly supervised manner. Experiments show that the proposed SAT achieves state-of-the-art performance on both CUB-200 and ImageNet, with 98.45% and 73.13% GT-known Loc.
  arXiv  Detail & Related papers  (2023-03-18T15:38:17Z)
• SLAN: Self-Locator Aided Network for Cross-Modal Understanding [89.20623874655352]
  We propose Self-Locator Aided Network (SLAN) for cross-modal understanding tasks. SLAN consists of a region filter and a region adaptor to localize regions of interest conditioned on different texts. It achieves fairly competitive results on five cross-modal understanding tasks.
  arXiv  Detail & Related papers  (2022-11-28T11:42:23Z)
• Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells [11.071527762096053]
  We propose a representation learning model called Space2Vec to encode the absolute positions and spatial relationships of places. Results show that because of its multi-scale representations, Space2Vec outperforms well-established ML approaches.
  arXiv  Detail & Related papers  (2020-02-16T04:22:18Z)

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.