Deep Fourier-embedded Network for Bi-modal Salient Object Detection

The rapid development of deep learning provides a significant improvement of salient object detection combining both RGB and thermal images. However, existing deep learning-based models suffer from two major shortcomings. First, the computation and memory demands of Transformer-based models with quadratic complexity are unbearable, especially in handling high-resolution bi-modal feature fusion. Second, even if learning converges to an ideal solution, there remains a frequency gap between the prediction and ground truth. Therefore, we propose a purely fast Fourier transform-based model, namely deep Fourier-embedded network (DFENet), for learning bi-modal information of RGB and thermal images. On one hand, fast Fourier transform efficiently fetches global dependencies with low complexity. Inspired by this, we design modal-coordinated perception attention to fuse the frequency gap between RGB and thermal modalities with multi-dimensional representation enhancement. To obtain reliable detailed information during decoding, we design the frequency-decomposed edge-aware module (FEM) to clarify object edges by deeply decomposing low-level features. Moreover, we equip proposed Fourier residual channel attention block in each decoder layer to prioritize high-frequency information while aligning channel global relationships. On the other hand, we propose co-focus frequency loss (CFL) to steer FEM towards minimizing the frequency gap. CFL dynamically weights hard frequencies during edge frequency reconstruction by cross-referencing the bi-modal edge information in the Fourier domain. This frequency-level refinement of edge features further contributes to the quality of the final pixel-level prediction. Extensive experiments on four bi-modal salient object detection benchmark datasets demonstrate our proposed DFENet outperforms twelve existing state-of-the-art models.

Efficient Fourier Filtering Network with Contrastive Learning for UAV-based Unaligned Bi-modal Salient Object Detection

Unmanned aerial vehicle (UAV)-based bi-modal salient object detection (BSOD) aims to segment salient objects in a scene utilizing complementary cues in unaligned RGB and thermal image pairs. However, the high computational expense of existing UAV-based BSOD models limits their applicability to real-world UAV devices. To address this problem, we propose an efficient Fourier filter network with contrastive learning that achieves both real-time and accurate performance. Specifically, we first design a semantic contrastive alignment loss to align the two modalities at the semantic level, which facilitates mutual refinement in a parameter-free way. Second, inspired by the fast Fourier transform that obtains global relevance in linear complexity, we propose synchronized alignment fusion, which aligns and fuses bi-modal features in the channel and spatial dimensions by a hierarchical filtering mechanism. Our proposed model, AlignSal, reduces the number of parameters by 70.0%, decreases the floating point operations by 49.4%, and increases the inference speed by 152.5% compared to the cutting-edge BSOD model (i.e., MROS). Extensive experiments on the UAV RGB-T 2400 and three weakly aligned datasets demonstrate that AlignSal achieves both real-time inference speed and better performance and generalizability compared to sixteen state-of-the-art BSOD models across most evaluation metrics. In addition, our ablation studies further verify AlignSal's potential in boosting the performance of existing aligned BSOD models on UAV-based unaligned data. The code is available at: https://github.com/JoshuaLPF/AlignSal.

Neural-Network-based NLOS Identification in Angular Domain at 60-GHz

This paper introduces an identification method that determines whether a millimeter-wave wireless transmission using directional antennas is being established over a line-of-sight (LOS) or a non-line-of-sight (NLOS) cluster for indoor localization applications. The proposed technique utilizes the channel power angular spectrum that is readily available from a beam training process. In particular, the behavior of five different channel metrics, namely the spatial-domain, time-domain, and frequency-domain channel kurtosis, the mean excess delay, and the RMS delay spread, is analyzed using maximum likelihood ratio and artificial neural network. A noticeable difference between LOS and NLOS clusters is observed and assessed for identification. Hypothesis testing shows errors as low as 0.01-0.02 in simulation and 0.04-0.07 in measurements at 60 GHz.