The paper proposes a multi‑branch conditional GAN that fuses visible, near‑infrared, and structural features for multispectral document binarization. By applying spatial‑ and channel‑wise softmax gating at encoder depth 3 and guiding the fusion with edge maps from selected VIS‑NIR band pairs, the authors evaluate nine configurations and find that the F3 + NIR edge‑guided model achieves the best performance, improving F1, p‑FM, and IoU over a fixed‑average baseline with statistically significant gains. The study demonstrates that targeted cross‑spectral structural guidance can effectively regulate intermediate fusion and enhance foreground‑overlap accuracy while preserving all four spectral bands.
arXiv:2512. 02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions.
The paper introduces Inverted Asymmetric Fusion (IAF) to address strong-modality collapse in multimodal learning, where dominant modalities are degraded during fusion. IAF preserves the dominant modality by passing it unchanged and letting weaker modalities attend to it, while also strengthening weaker modalities via Modality-Aware Knowledge Distillation. Experiments on MultiHuSE, UR-FUNNY, and MUStARD show that IAF maintains unimodal performance and improves over the best unimodal baseline by up to 8.25%.
arXiv:2607. 08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions.
arXiv:2602.10137v2 Announce Type: replace Abstract: This work proposes MeCSAFNet, a multi-branch encoder-decoder architecture for land cover segmentation in multispectral imagery. The model separatel...
The paper introduces a controlled anchor–stress protocol to analyze compositional failures in text‑to‑image diffusion models. It presents a text‑only Compositional Stress Index (CSI) that distinguishes common from rare compositions across SD1.5, SDXL, and SD3, and conducts a six‑prompt localization study showing how different denoiser interventions affect outcomes such as residual minimization and color hit rate. The study reveals that diagnostic signals appear before denoising, but the intervention that exposes risk is not always the one that improves generation, highlighting modality‑ and architecture‑dependent responses.
arXiv:2608.06205v2 Announce Type: replace Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
DeepMine-Mamba addresses information dilution in Mamba-based state space models for document image binarization. The authors show that state-space propagation amplifies both foreground and background, reducing contrast, and introduce an Anti‑Dilution Gate that uses propagation‑induced differences and a learned stroke prior to preserve stroke details. On DIBCO/H‑DIBCO benchmarks without external data, the method matches transformer and gated‑convolution baselines and improves all four evaluation metrics.
arXiv:2603.16130v5 Announce Type: replace Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in vis...
arXiv:2603. 06673v2 Announce Type: replace-cross Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts.
arXiv:2601. 03100v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized.
The paper presents M2Heat, a physics-inspired framework for fusing hyperspectral and LiDAR data in land‑cover classification. It introduces a visual heat conduction module (vHeat) and Frequency Value Embeddings (FVEs) to model anisotropic information flow, capturing global dependencies with sub‑quadratic complexity. Combined with a Cross‑Frequency Fusion (CFF) module, M2Heat delivers discriminative, robust features and achieves competitive performance on Trento, Houston2013, and Augsburg benchmarks while offering an interpretable heat‑conduction perspective.
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.