IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]

Image restoration seeks to reconstruct high-quality images from low-quality observations, with tasks such as superresolution, deblurring, and inpainting. Recently, diffusion models have emerged as powerful tools for image restoration, generating diverse and high-quality samples. However, their high computational demands, including multiple denoising steps, hinder deployment on resource-constrained devices like smartphones. While efforts to optimize sampling trajectories have been made, the large parameters of DMs remain a challenge. Model quantization, a common solution for reducing latency and memory usage, faces channel-wise variance when applied to DMs. Thus, an incoherence processing is needed for the outlier suppression to produce approximately Gaussian distribution. Futhermore, the uniform bitwidth allocation also turns into the bottleneck of quantization due to the ignorance of layer-wise sensitivity. To address the two main challenges, we propose MPRDiff, a mixed-precision quantization method that uses a computationally efficient randomized Hadamard transform technique to eliminate channel-wise incoherency and enable W3.8A4 layer-wise quantization of the DMs. Additionally, we use integer linear programming for optimal mixed bitwidth allocation and incorporate sampling step correction techniques. MPRDiff is evaluated on CelebA and ImageNet, demonstrating a minimal increase in FID while significantly reducing model size.

In this paper, we introduce IRM-Net, a novel variant of the U-Net architecture designed for indoor radio map estimation. IRM-Net incorporates two principal improvements over the standard U-Net. First, we replace conventional convolutional layers with a cascaded combination of a Detail Enhancement Block (DEB) and a Detail Enhancement Attention Block (DEAB), which enhances the model's ability to capture finegrained features. Second, we implement dense connections in both the encoder and decoder, facilitating multi-level semantic interactions that mitigate information loss more effectively than traditional serial connections. IRM-Net was trained and evaluated on the benchmark dataset provided by the Sampling-Assisted Pathloss Radio Map Prediction Data Competition. Experimental results demonstrate that our approach can reliably predict path loss distributions in previously unseen indoor environments.

Computer simulations across various scientific domains often necessitate efficient calibration methods to ensure accurate representation of real-world phenomena. Traditional approaches rely heavily on expert intervention and high computational resources, which can be limiting. In this paper, we propose a calibration framework based on the model-bridge (MB) paradigm, which addresses these problems. Our MB method has two components: a surrogate and a bridge. Our surrogate is a Hankel correlation matrix, which is built by treating simulation systems as signals, representing their frequency-amplitude information compactly. This surrogate allows us to flexibly deal with different time lengths and simulation behaviors, and provides a straightforward geometric interpretation enabled by the underlying matrix manifold geometry. For the bridge model, we propose a kernel ridge regression approach and multiple kernel functions based on matrix manifold metrics. Through experimental validation on synthetic signal and fluid dynamics simulations, we show that the proposed Hankel framework outperforms existing calibration methods.

Multispectral object detection based on visible and infrared images leverages the complementary advantages of both modalities for robust object detection across both day and night conditions. However, existing methods only employ single-stage fusion paradigm, which lack hierarchical information interaction for deep feature fusion. To address this, we propose a novel multispectral coarse-to-fine fusion approach for object detection (MCFF-Det). MCFF-Det features dual fusion stages developed based on YOLOv8 and transformer, performing coarse and fine fusion, respectively. Specifically, the fine fusion stage introduces a transformer-based fusion refinement (TFR) module to refine the coarse-fused features on a patchwise basis and reintroduce the original modalities to complete the cross-modal fusion. Extensive experiments conducted on the LLVIP dataset demonstrate that MCFF-Det outperforms state-of-the-art methods quantitatively and qualitatively in terms of pedestrian detection across diverse scenarios.

Over the last decade, deep learning models have been widely used for automatic feature extraction and classification in various Brain-Computer Interface (BCI) tasks. However, their performance and generalization capabilities are often not adequately assessed, as these models are frequently trained and tested under flawed setups and / or influenced by spurious correlations. Recently, these limitations have also been observed in the training and evaluation of Large Brainwave Foundation Models (LBMs). In this work, we employ causal reasoning and careful consideration for task-discriminative artifacts in various EEG datasets covering diverse BCI paradigms and propose a benchmarking protocol to properly evaluate the decoding performance and generalization capabilities of LBMs. Utilising a subject-independent cross-validation approach for each curated benchmark dataset, we showcase that LBMs achieve marginal performance gains over conventional deep learning baselines.