← Back to all news
arXiv Computer Vision By Yasas Savinda, Maheshi Buddhinee Dissanayake

Cross-Spectral Feature Fusion for Multispectral Document Binarization

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Aug 28

Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion

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%.

By Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat
efficiencymultimodalbenchmarks
More like this →
arXiv Computer Vision
Oct 5

Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition

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.

By Fangzheng Wu, Brian Summa
ragdiffusionbenchmarks
More like this →