AIM: Adaptive Intra-Network Modulation for Balanced Multimodal Learning

Multimodal learning has significantly enhanced machine learning performance but still faces numerous challenges and limitations. Imbalanced multimodal learning is one of the problems extensively studied in recent works and is typically mitigated by modulating the learning of each modality. However, we find that these methods typically hinder the dominant modality's learning to promote weaker modalities, which affects overall multimodal performance. We analyze the cause of this issue and highlight a commonly overlooked problem: optimization bias within networks. To address this, we propose Adaptive Intra-Network Modulation (AIM) to improve balanced modality learning. AIM accounts for differences in optimization state across parameters and depths within the network during modulation, achieving balanced multimodal learning without hindering either dominant or weak modalities for the first time. Specifically, AIM decouples the dominant modality's under-optimized parameters into Auxiliary Blocks and encourages reliance on these performance-degraded blocks for joint training with weaker modalities. This approach effectively prevents suppression of weaker modalities while enabling targeted optimization of under-optimized parameters to improve the dominant modality. Additionally, AIM assesses modality imbalance level across network depths and adaptively adjusts modulation strength at each depth. Experimental results demonstrate that AIM outperforms state-of-the-art imbalanced modality learning methods across multiple benchmarks and exhibits strong generalizability across different backbones, fusion strategies, and optimizers.

Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection

Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management. Despite the remarkable progress of deep learning in recent years, most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions. We observe that frequency-domain feature modeling particularly in the wavelet domain an amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain. Thus, we propose a method called Wavelet-Guided Dual-Frequency Encoding (WGDF). Specifically, we first apply Discrete Wavelet Transform (DWT) to decompose the input images into high-frequency and low-frequency components, which are used to model local details and global structures, respectively. In the high-frequency branch, we design a Dual-Frequency Feature Enhancement (DFFE) module to strengthen edge detail representation and introduce a Frequency-Domain Interactive Difference (FDID) module to enhance the modeling of fine-grained changes. In the low-frequency branch, we exploit Transformers to capture global semantic relationships and employ a Progressive Contextual Difference Module (PCDM) to progressively refine change regions, enabling precise structural semantic characterization. Finally, the high- and low-frequency features are synergistically fused to unify local sensitivity with global discriminability. Extensive experiments on multiple remote sensing datasets demonstrate that WGDF significantly alleviates edge ambiguity and achieves superior detection accuracy and robustness compared to state-of-the-art methods. The code will be available at https://github.com/boshizhang123/WGDF.

DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal

Document shadow removal is a crucial task in the field of document image enhancement. However, existing methods tend to remove shadows with constant color background and ignore color shadows. In this paper, we first design a diffusion model in latent space for document image shadow removal, called DocShaDiffusion. It translates shadow images from pixel space to latent space, enabling the model to more easily capture essential features. To address the issue of color shadows, we design a shadow soft-mask generation module (SSGM). It is able to produce accurate shadow mask and add noise into shadow regions specially. Guided by the shadow mask, a shadow mask-aware guided diffusion module (SMGDM) is proposed to remove shadows from document images by supervising the diffusion and denoising process. We also propose a shadow-robust perceptual feature loss to preserve details and structures in document images. Moreover, we develop a large-scale synthetic document color shadow removal dataset (SDCSRD). It simulates the distribution of realistic color shadows and provides powerful supports for the training of models. Experiments on three public datasets validate the proposed method's superiority over state-of-the-art. Our code and dataset will be publicly available.

10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection

The widespread deployment of InfRared Small-Target Detection(IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binary neural networks (BNNs) are distinguished by their exceptional efficiency in model compression. However, the small size of infrared targets introduces stringent precision requirements for the IRSTD task, while the inherent precision loss during binarization presents a significant challenge. To address this, we propose the Binarized Infrared Small-Target Detection Network (BiisNet), which preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow. Specifically, we propose the Dot-Binary Convolution, which retains fine-grained semantic information in feature maps while still leveraging the binarized convolution operations. In addition, we introduce a smooth and adaptive Dynamic Softsign function, which provides more comprehensive and progressively finer gradient during back-propagation, enhancing model stability and promoting an optimal weight distribution.Experimental results demonstrate that BiisNet not only significantly outperforms other binary architectures but also demonstrates strong competitiveness among state-of-the-art full-precision models.

Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contexutual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies both image augmentation and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under the circumstance of limited fine annotations.

MICINet: Multi-Level Inter-Class Confusing Information Removal for Reliable Multimodal Classification

Reliable multimodal learning in the presence of noisy data is a widely concerned issue, especially in safety-critical applications. Many reliable multimodal methods delve into addressing modality-specific or cross-modality noise. However, they fail to handle the coexistence of both types of noise efficiently. Moreover, the lack of comprehensive consideration for noise at both global and individual levels limits their reliability. To address these issues, a reliable multimodal classification method dubbed Multi-Level Inter-Class Confusing Information Removal Network (MICINet) is proposed. MICINet achieves the reliable removal of both types of noise by unifying them into the concept of Inter-class Confusing Information (\textit{ICI}) and eliminating it at both global and individual levels. Specifically, MICINet first reliably learns the global \textit{ICI} distribution through the proposed \textbf{\textit{Global \textbf{ICI} Learning Module}}. Then, it introduces the \textbf{\textit{Global-guided Sample ICI Learning module}} to efficiently remove global-level \textit{ICI} from sample features utilizing the learned global \textit{ICI} distribution. Subsequently, the \textbf{\textit{Sample-adaptive Cross-modality Information Compensation module}} is designed to remove individual-level \textit{ICI} from each sample reliably. This is achieved through interpretable cross-modality information compensation based on the complementary relationship between discriminative features and \textit{ICI} and the perception of the relative quality of modalities introduced by the relative discriminative power. Experiments on four datasets demonstrate that MICINet outperforms other state-of-the-art reliable multimodal classification methods under various noise conditions.

Online-BLS: An Accurate and Efficient Online Broad Learning System for Data Stream Classification

The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end, we introduce an online broad learning system framework with closed-form solutions for each online update. Different from employing existing incremental broad learning algorithms for online learning tasks, which tend to incur degraded accuracy and expensive online update overhead, we design an effective weight estimation algorithm and an efficient online updating strategy to remedy the above two deficiencies, respectively. Specifically, an effective weight estimation algorithm is first developed by replacing notorious matrix inverse operations with Cholesky decomposition and forward-backward substitution to improve model accuracy. Second, we devise an efficient online updating strategy that dramatically reduces online update time. Theoretical analysis exhibits the splendid error bound and low time complexity of our model. The most popular test-then-training evaluation experiments on various real-world datasets prove its superiority and efficiency. Furthermore, our framework is naturally extended to data stream scenarios with concept drift and exceeds state-of-the-art baselines.

Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer

In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic complexity of attention mechanisms, stereo transformers still fail to demonstrate sufficient nonlinear expressiveness within a reasonable inference time. The lack of focus on key homonymous points renders the representations of such methods vulnerable to challenging conditions, including reflections and weak textures. Furthermore, a slow computing speed is not conducive to the application. To overcome these difficulties, we present the \textbf{H}adamard \textbf{A}ttention \textbf{R}ecurrent Stereo \textbf{T}ransformer (HART) that incorporates the following components: 1) For faster inference, we present a Hadamard product paradigm for the attention mechanism, achieving linear computational complexity. 2) We designed a Dense Attention Kernel (DAK) to amplify the differences between relevant and irrelevant feature responses. This allows HART to focus on important details. DAK also converts zero elements to non-zero elements to mitigate the reduced expressiveness caused by the low-rank bottleneck. 3) To compensate for the spatial and channel interaction missing in the Hadamard product, we propose MKOI to capture both global and local information through the interleaving of large and small kernel convolutions. Experimental results demonstrate the effectiveness of our HART. In reflective area, HART ranked \textbf{1st} on the KITTI 2012 benchmark among all published methods at the time of submission. Code is available at \url{https://github.com/ZYangChen/HART}.

QADM-Net: Quality-adaptive Dynamic Network for Reliable Multimodal Classification

Integrating complementary information from different data modalities can yield representation with stronger expressive ability. However, data quality varies across multimodal samples, highlighting the need for learning reliable multimodal representations, especially in safety-critical applications. This paper focuses on an aspect that existing methods in this domain commonly overlook: the importance of network dynamics and adaptability in providing reliable results from diverse samples. Specifically, it highlights the model's ability to dynamically adjust its capacity and behaviour according to different samples, using the adjusted network for predicting each sample. To this end, we propose a novel framework for multimodal reliable classification termed Quality-adaptive Dynamic Multimodal Network (QADM-Net). QADM-Net first introduces a confidence-guided dynamic depths mechanism to achieve the appropriate network capacity. This mechanism adjusts the network depth according to the difficulty of each sample, which is determined by the quality of its modalities. Subsequently, we develop an informativeness-based dynamic parameters mechanism that enables QADM-Net to perform unique inference behaviour on each of the diverse samples with feature-level quality variation presented in their feature vectors. In this way, QADM-Net adequately adapts its capacity and behaviour on each sample by investigating the quality variation of samples at both modality and feature levels, thus enhancing the reliability of classification results. Experiments conducted on four datasets demonstrate that QADM-Net significantly outperforms state-of-the-art methods in classification performance and exhibits strong adaptability to data with diverse quality.

A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System

Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints have emerged in recent TSAD research. Deep learning is not required for TSAD due to limitations such as slow deep learning speed. The Broad Learning System (BLS) is a shallow network framework that benefits from its ease of optimization and speed. It has been shown to outperform machine learning approaches while remaining competitive with deep learning. Based on the current situation of TSAD, we propose the Contrastive Patch-based Broad Learning System (CPatchBLS). This is a new exploration of patching technique and BLS, providing a new perspective for TSAD. We construct Dual-PatchBLS as a base through patching and Simple Kernel Perturbation (SKP) and utilize contrastive learning to capture the differences between normal and abnormal data under different representations. To compensate for the temporal semantic loss caused by various patching, we propose CPatchBLS with model level integration, which takes advantage of BLS's fast feature to build model-level integration and improve model detection. Using five real-world series anomaly detection datasets, we confirmed the method's efficacy, outperforming previous deep learning and machine learning methods while retaining a high level of computing efficiency.