Multispectral object detection is a fundamental task with an extensive range of practical implications. In particular, combining visible (RGB) and infrared (IR) images can offer complementary information that enhances detection performance in different weather scenarios. However, the existing methods generally involve aligning features across modalities and require proposals for the two-stage detectors, which are often slow and unsuitable for large-scale applications. To overcome this challenge, we introduce a novel one-stage oriented detector for RGB-infrared object detection called the Layer-wise Cross-Modality calibration and Aggregation (LCMA) detector. LCMA employs a layer-wise strategy to achieve cross-modality alignment by using the proposed inter-modality spatial-reduction attention. Moreover, we design Gated Coupled Filter in each layer to capture semantically meaningful features while ensuring that well-aligned and foreground object information is obtained before forwarding them to the detection head. This relieves the need for a region proposal step for the alignment, enabling direct category and bounding box predictions in a unified one-stage oriented detector. Extensive experiments on two challenging datasets demonstrate that the proposed LCMA outperforms state-of-the-art methods in terms of both accuracy and computational efficiency, which implies the efficacy of our approach in exploiting multi-modality information for robust and efficient multispectral object detection.

Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in hyperspectral images faces significant challenges in intra- and inter-class similarity due to the spatial differences in hyperspectral inter-bands and unavoidable interferences, e.g., sensor noises and illumination. To alleviate the hyperspectral inter-bands inconsistencies and redundancy, we propose a novel network termed \textbf{S}pectral \textbf{D}iscrepancy and \textbf{C}ross-\textbf{M}odal semantic consistency learning (SDCM), which facilitates the extraction of consistent information across a wide range of hyperspectral bands while utilizing the spectral dimension to pinpoint regions of interest. Specifically, we leverage a semantic consistency learning (SCL) module that utilizes inter-band contextual cues to diminish the heterogeneity of information among bands, yielding highly coherent spectral dimension representations. On the other hand, we incorporate a spectral gated generator (SGG) into the framework that filters out the redundant data inherent in hyperspectral information based on the importance of the bands. Then, we design the spectral discrepancy aware (SDA) module to enrich the semantic representation of high-level information by extracting pixel-level spectral features. Extensive experiments on two hyperspectral datasets demonstrate that our proposed method achieves state-of-the-art performance when compared with other ones.

Spatial transcriptomics enables gene expression profiling with spatial context, offering unprecedented insights into the tissue microenvironment. However, most computational models treat genes as isolated numerical features, ignoring the rich biological semantics encoded in their symbols. This prevents a truly deep understanding of critical biological characteristics. To overcome this limitation, we present SemST, a semantic-guided deep learning framework for spatial transcriptomics data clustering. SemST leverages Large Language Models (LLMs) to enable genes to "speak" through their symbolic meanings, transforming gene sets within each tissue spot into biologically informed embeddings. These embeddings are then fused with the spatial neighborhood relationships captured by Graph Neural Networks (GNNs), achieving a coherent integration of biological function and spatial structure. We further introduce the Fine-grained Semantic Modulation (FSM) module to optimally exploit these biological priors. The FSM module learns spot-specific affine transformations that empower the semantic embeddings to perform an element-wise calibration of the spatial features, thus dynamically injecting high-order biological knowledge into the spatial context. Extensive experiments on public spatial transcriptomics datasets show that SemST achieves state-of-the-art clustering performance. Crucially, the FSM module exhibits plug-and-play versatility, consistently improving the performance when integrated into other baseline methods.