SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization

• URL: http://arxiv.org/abs/2601.06222v1
• Date: Fri, 09 Jan 2026 07:25:55 GMT
• Title: SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization
• Authors: Xinghao Wang, Changtao Miao, Dianmo Sheng, Tao Gong, Qi Chu, Nenghai Yu, Quanchen Zou, Deyue Zhang, Xiangzheng Zhang,
• Abstract summary: Malicious image manipulation threatens public safety and requires efficient localization methods.<n>Existing weakly supervised methods rely on image-level binary labels and focus on global classification.<n>We propose Semantic-Agnostic Prompt Learning (SAPL) in CLIP, which learns text prompts that intentionally encode non-semantic, boundary-centric cues.
• Score: 45.19935082419337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Malicious image manipulation threatens public safety and requires efficient localization methods. Existing approaches depend on costly pixel-level annotations which make training expensive. Existing weakly supervised methods rely only on image-level binary labels and focus on global classification, often overlooking local edge cues that are critical for precise localization. We observe that feature variations at manipulated boundaries are substantially larger than in interior regions. To address this gap, we propose Semantic-Agnostic Prompt Learning (SAPL) in CLIP, which learns text prompts that intentionally encode non-semantic, boundary-centric cues so that CLIPs multimodal similarity highlights manipulation edges rather than high-level object semantics. SAPL combines two complementary modules Edge-aware Contextual Prompt Learning (ECPL) and Hierarchical Edge Contrastive Learning (HECL) to exploit edge information in both textual and visual spaces. The proposed ECPL leverages edge-enhanced image features to generate learnable textual prompts via an attention mechanism, embedding semantic-irrelevant information into text features, to guide CLIP focusing on manipulation edges. The proposed HECL extract genuine and manipulated edge patches, and utilize contrastive learning to boost the discrimination between genuine edge patches and manipulated edge patches. Finally, we predict the manipulated regions from the similarity map after processing. Extensive experiments on multiple public benchmarks demonstrate that SAPL significantly outperforms existing approaches, achieving state-of-the-art localization performance.

Related papers

• FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding [27.61517650555534]
  Current RS image-text datasets generate global captions from object-level labels.<n>Their direct application to RS data often leads to performance degradation.<n>We propose FarSLIP, a Fine-grained RS LanguageImage Pretraining framework.
  arXiv  Detail & Related papers  (2025-11-18T20:39:15Z)
• Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation [19.26516470653798]
  Weakly Supervised Semantic (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Maps (CAMs)<n>Recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored.<n>We propose ExCEL to explore CLIP's dense knowledge via a novel patchtext alignment paradigm for WSSS.
  arXiv  Detail & Related papers  (2025-03-26T02:00:49Z)
• Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection [54.21851618853518]
  We present a concise yet effective approach called Patch Generation-to-Selection to enhance CLIP's training efficiency.<n>Our approach, CLIP-PGS, sets new state-of-the-art results in zero-shot classification and retrieval tasks.
  arXiv  Detail & Related papers  (2025-03-21T12:10:38Z)
• FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic Segmentation [63.31007867379312]
  Open-vocabulary segmentation aims to identify and segment specific regions and objects based on text-based descriptions.<n>A common solution is to leverage powerful vision-language models (VLMs), such as CLIP, to bridge the gap between vision and text information.<n>In contrast, segmentation tasks require fine-grained pixel-level alignment and detailed category boundary information.<n>We propose FGAseg, a model designed for fine-grained pixel-text alignment and category boundary supplementation.
  arXiv  Detail & Related papers  (2025-01-01T15:47:04Z)
• Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation [56.001484215308075]
  We present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP.<n>We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process.<n> Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.
  arXiv  Detail & Related papers  (2024-11-28T19:00:03Z)
• DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation [8.422110274212503]
  Weakly supervised semantic segmentation approaches typically rely on class activation maps (CAMs) for initial seed generation. We introduce DALNet, which leverages text embeddings to enhance the comprehensive understanding and precise localization of objects across different levels of granularity. Our approach, in particular, allows for more efficient end-to-end process as a single-stage method.
  arXiv  Detail & Related papers  (2024-09-24T06:51:49Z)
• Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision [23.931443799102663]
  We introduce a Multi-Grained Cross-modal Alignment (MGCA) framework to bridge the granularity gap without any dense annotations. Specifically, MGCA constructs pseudo multi-granular semantic correspondences upon image-text pairs. Our method achieves significant advancements over state-of-the-art methods, demonstrating its effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-03-06T13:43:36Z)
• CLIM: Contrastive Language-Image Mosaic for Region Representation [58.05870131126816]
  Contrastive Language-Image Mosaic (CLIM) is a novel approach for aligning region and text representations. CLIM consistently improves different open-vocabulary object detection methods. It can effectively enhance the region representation of vision-language models.
  arXiv  Detail & Related papers  (2023-12-18T17:39:47Z)
• Towards Effective Image Manipulation Detection with Proposal Contrastive Learning [61.5469708038966]
  We propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noise views respectively. Our PCL can be easily adapted to unlabeled data in practice, which can reduce manual labeling costs and promote more generalizable features.
  arXiv  Detail & Related papers  (2022-10-16T13:30:13Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)

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.