Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention

• URL: http://arxiv.org/abs/2603.20729v1
• Date: Sat, 21 Mar 2026 09:28:05 GMT
• Title: Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention
• Abstract summary: We introduce a weakly supervised multimodal segmentation framework that refines threshold-guided pseudo-labels through learned models.<n>We establish that threshold-guided learned refinement provides the most robust improvement over raw thresholding.<n>The strongest model, confidence-gated depth-aware cross-attention (CG-DCA), consistently outperforms threshold-based, image-only, and earlier multimodal baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acoustic borehole images provide high-resolution borehole-wall structure, but large-scale interpretation remains difficult because dense expert annotations are rarely available and subsurface information is intrinsically multimodal. The challenge is developing weakly supervised methods combining two-dimensional image texture with depth-aligned one-dimensional well-logs. Here, we introduce a weakly supervised multimodal segmentation framework that refines threshold-guided pseudo-labels through learned models. This preserves the annotation-free character of classical thresholding and clustering workflows while extending them with denoising, confidence-aware pseudo-supervision, and physically structured fusion. We establish that threshold-guided learned refinement provides the most robust improvement over raw thresholding, denoised thresholding, and latent clustering baselines. Multimodal performance depends strongly on fusion strategy: direct concatenation provides limited gains, whereas depth-aware cross-attention, gated fusion, and confidence-aware modulation substantially improve agreement with the weak supervisory reference. The strongest model, confidence-gated depth-aware cross-attention (CG-DCA), consistently outperforms threshold-based, image-only, and earlier multimodal baselines. Targeted ablations show its advantage depends specifically on confidence-aware fusion and structured local depth interaction rather than model complexity alone. Cross-well analyses confirm this performance is broadly stable. These results establish a practical, scalable framework for annotation-free segmentation, showing multimodal improvement is maximized when auxiliary logs are incorporated selectively and depth-aware.

Related papers

• More Perspectives, Stronger Signals: Multi-Perspective Enhancement and Progressive Fusion for Multimodal Entity Representation Learning [6.4639268448773946]
  We propose PrismF, a unified framework that synergizes multi-perspective enhancement with progressive fusion to extract stronger signals from diverse inputs.<n>PrismF enhances fine-grained intra-modal semantics through a multi-perspective mechanism that decomposes each modality into complementary views.<n>Experiments show that PrismF achieves the strongest overall performance, including relative improvements of 4.04% in MRR and 11.17% in Hits@1 on KVC16K.
  arXiv  Detail & Related papers  (2026-08-29T08:29:43Z)
• M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo [7.74235950208606]
  Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes.<n>We propose a novel framework that overcomes this limitation by tightly coupling a DFM with a cascade MVS pipeline.<n>Our method leverages MVS depth to resolve the scale ambiguity in monocular predictions, while the monocular depth, in turn, enhances the structural completeness and fine-grained detail of the MVS estimate.
  arXiv  Detail & Related papers  (2026-08-21T07:01:42Z)
• EVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration [14.088182834458843]
  We propose EVAS, an end-to-end framework for temporal forgery localization.<n>We introduce a Boundary-Aware Refinement strategy to achieve steered boundary calibration.<n> EVAS achieves state-of-the-art average localization accuracy and average recall across three benchmark datasets.
  arXiv  Detail & Related papers  (2026-07-05T19:33:21Z)
• Visual Enhanced Depth Scaling for Multimodal Latent Reasoning [32.211888127924446]
  Multimodal latent reasoning has emerged as a promising paradigm that replaces explicit Chain-of-Thought decoding with implicit feature propagation.<n>We propose a visual replay module and routing depth scaling to collaboratively enhance visual perception and refine complicated latents for deeper contextual reasoning.<n>Our framework achieves state-of-the-art performance across diverse benchmarks while delivering substantial inference speedups over explicit CoT baselines.
  arXiv  Detail & Related papers  (2026-04-12T07:14:30Z)
• EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection [20.892452296357813]
  Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings.<n>We propose a unified UCOD framework that enhances both the reliability of pseudo-labels and the fidelity of features.<n>Our method achieves state-of-the-art performance, characterized by superior detail perception, robust boundary alignment, and strong generalization.
  arXiv  Detail & Related papers  (2026-03-12T04:10:32Z)
• Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild [12.893641381621459]
  We propose a novel multimodal framework driven by Hierarchical Granularity Alignment and State Space Models.<n>We overcome the receptive field limitations of conventional temporal convolutional networks by introducing a Vision-Mamba architecture.<n>This approach enables temporal modeling with O(N) linear complexity, effectively capturing ultra-long-range dynamics without performance degradation.
  arXiv  Detail & Related papers  (2026-03-11T21:05:59Z)
• CrystaL: Spontaneous Emergence of Visual Latents in MLLMs [55.34169914483764]
  We propose CrystaL (Crystallized Latent Reasoning), a single-stage framework with two paths to process intact and corrupted images.<n>By explicitly aligning the attention patterns and prediction distributions across the two paths, CrystaL crystallizes latent representations into task-relevant visual semantics.<n>Experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-24T15:01:30Z)
• Multi-label Classification with Panoptic Context Aggregation Networks [61.82285737410154]
  This paper introduces the Deep Panoptic Context Aggregation Network (PanCAN), a novel approach that hierarchically integrates multi-order geometric contexts.<n>PanCAN learns multi-order neighborhood relationships at each scale by combining random walks with an attention mechanism.<n>Experiments on NUS-WIDE, PASCAL VOC,2007, and MS-COCO benchmarks demonstrate that PanCAN consistently achieves competitive results.
  arXiv  Detail & Related papers  (2025-12-29T14:16:21Z)
• Extremal Contours: Gradient-driven contours for compact visual attribution [5.6220652636435915]
  We show how a star-supervised framework can achieve higher complexity with dense masks.<n>On ImageNets, it matches the extremal contours of dense masks while producing compact regions with improved run-to-run vision.
  arXiv  Detail & Related papers  (2025-11-03T10:02:21Z)
• WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition [61.3530659856013]
  We propose a novel decoder architecture, WaveSeg, which jointly optimize feature refinement in spatial and wavelet domains.<n>High-frequency components are first learned from input images as explicit priors to reinforce boundary details.<n>Experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-10-24T01:41:31Z)
• Explaining multimodal LLMs via intra-modal token interactions [55.27436637894534]
  Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse vision-language tasks, yet their internal decision-making mechanisms remain insufficiently understood.<n>We propose enhancing interpretability by leveraging intra-modal interaction.
  arXiv  Detail & Related papers  (2025-09-26T14:39:13Z)
• Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection [12.743278093269325]
  We propose a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet)<n>DUGC is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance.<n>MCF uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features.
  arXiv  Detail & Related papers  (2025-08-28T04:31:48Z)
• Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation [51.14107156747967]
  Weakly supervised semantic segmentation (WSSS) has attracted considerable attention because it requires fewer annotations than fully supervised approaches.<n>We propose an Adaptive Re-Activation Mechanism (AReAM) to control deep-level attention to undisciplined over-smoothing.<n>AReAM substantially improves segmentation performance compared with existing WSSS methods, reducing noise while sharpening focus on relevant semantic regions.
  arXiv  Detail & Related papers  (2023-05-04T19:11:33Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)

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.