TL;DR
STMFusion is proposed, a novel Mamba-based semantic-guided fusion network that adopts a Cross-Modulated Fusion Block (CMFB), which leverages semantic information to guide the fusion process and integrates cross-modal features through a shared state modulation strategy.
Objectives. We propose a modern method for semantic segmentation of ultra-high-resolution (4K) video frames in the field of Earth remote sensing using a modification of the DeepLabV3+ convolutional neural network. Methods. The method is aimed at solving two critical problems: the limited receptive field of the model w...
Object detection using visible–infrared images has become increasingly important for all-day detection scenarios. However, due to significant imaging discrepancies between the visible and infrared modalities, achieving accurate modal alignment and effective feature fusion remains a major challenge. Existing methods oft...
STSFusion is proposed, a segmentation task-driven spatial-frequency collaborative fusion method for infrared and visible images that achieves superior fusion performance over state-of-the-art methods and shows favorable task adaptability in downstream semantic segmentation and object detection.
Mamba, a global context modeling paradigm with a selective scanning mechanism, has recently attracted increasing attention in multimodal image fusion. Multimodal fusion aims to preserve and enhance cross-modal complementary information to produce high-quality fused images. Despite its suitability, existing Mamba-based...
: Multimodal learning has emerged as a critical paradigm in artificial intelligence, enabling systems to integrate information from diverse data sources such as text, images, audio, and video. However, effectively fusing heterogeneous modalities remains challenging due to differences in feature dimensions, semantic rep...
While Mamba-based models have shown strong potential for long sequence modeling, adapting them to vision is challenging due to the requirement of local neighborhood correlations and multi-directional spatial contexts for visual understanding. In this paper, we present LiAuto-MindViT, a novel hybrid vision backbone that...
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