Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network

Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound.

LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict the lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposing LC-LLM, an explainable lane change prediction model that leverages the strong reasoning capabilities and self-explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information in natural language as prompts for input into the LLM and employing a supervised fine-tuning technique to tailor the LLM specifically for our lane change prediction task. This allows us to utilize the LLM's powerful common sense reasoning abilities to understand complex interactive information, thereby improving the accuracy of long-term predictions. Furthermore, we incorporate explanatory requirements into the prompts in the inference stage. Therefore, our LC-LLM model not only can predict lane change intentions and trajectories but also provides explanations for its predictions, enhancing the interpretability. Extensive experiments on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can encode comprehensive interaction information for driving behavior understanding.

Toward Interactive Regional Understanding in Vision-Large Language Models

Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements. Nevertheless, these models heavily rely on image-text pairs that capture only coarse and global information of an image, leading to a limitation in their regional understanding ability. In this work, we introduce \textbf{RegionVLM}, equipped with explicit regional modeling capabilities, allowing them to understand user-indicated image regions. To achieve this, we design a simple yet innovative architecture, requiring no modifications to the model architecture or objective function. Additionally, we leverage a dataset that contains a novel source of information, namely Localized Narratives, which has been overlooked in previous VLP research. Our experiments demonstrate that our single generalist model not only achieves an interactive dialogue system but also exhibits superior performance on various zero-shot region understanding tasks, without compromising its ability for global image understanding.

H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images

Positron emission tomography (PET) combined with computed tomography (CT) imaging is routinely used in cancer diagnosis and prognosis by providing complementary information. Automatically segmenting tumors in PET/CT images can significantly improve examination efficiency. Traditional multi-modal segmentation solutions mainly rely on concatenation operations for modality fusion, which fail to effectively model the non-linear dependencies between PET and CT modalities. Recent studies have investigated various approaches to optimize the fusion of modality-specific features for enhancing joint representations. However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure. To address these issues, we propose a Hierarchical Adaptive Interaction and Weighting Network termed H2ASeg to explore the intrinsic cross-modal correlations and transfer potential complementary information. Specifically, we design a Modality-Cooperative Spatial Attention (MCSA) module that performs intra- and inter-modal interactions globally and locally. Additionally, a Target-Aware Modality Weighting (TAMW) module is developed to highlight tumor-related features within multi-modal features, thereby refining tumor segmentation. By embedding these modules across different layers, H2ASeg can hierarchically model cross-modal correlations, enabling a nuanced understanding of both semantic and structural tumor features. Extensive experiments demonstrate the superiority of H2ASeg, outperforming state-of-the-art methods on AutoPet-II and Hecktor2022 benchmarks. The code is released at https://github.com/JinPLu/H2ASeg.

Hypergraph-based Multi-View Action Recognition using Event Cameras

Action recognition from video data forms a cornerstone with wide-ranging applications. Single-view action recognition faces limitations due to its reliance on a single viewpoint. In contrast, multi-view approaches capture complementary information from various viewpoints for improved accuracy. Recently, event cameras have emerged as innovative bio-inspired sensors, leading to advancements in event-based action recognition. However, existing works predominantly focus on single-view scenarios, leaving a gap in multi-view event data exploitation, particularly in challenges like information deficit and semantic misalignment. To bridge this gap, we introduce HyperMV, a multi-view event-based action recognition framework. HyperMV converts discrete event data into frame-like representations and extracts view-related features using a shared convolutional network. By treating segments as vertices and constructing hyperedges using rule-based and KNN-based strategies, a multi-view hypergraph neural network that captures relationships across viewpoint and temporal features is established. The vertex attention hypergraph propagation is also introduced for enhanced feature fusion. To prompt research in this area, we present the largest multi-view event-based action dataset $\text{THU}^{\text{MV-EACT}}\text{-50}$, comprising 50 actions from 6 viewpoints, which surpasses existing datasets by over tenfold. Experimental results show that HyperMV significantly outperforms baselines in both cross-subject and cross-view scenarios, and also exceeds the state-of-the-arts in frame-based multi-view action recognition.

3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation

Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requirements for segmentation efficiency. Convolutional neural networks (CNNs) have made considerable achievements in MIS. However, they are difficult to fully collect global context information and their pooling layer may cause information loss. Capsule networks, which combine the benefits of CNNs while taking into account additional information such as relative location that CNNs do not, have lately demonstrated some advantages in MIS. Vision Transformer (ViT) employs transformers in visual tasks. Transformer based on attention mechanism has excellent global inductive modeling capabilities and is expected to capture longrange information. Moreover, there have been resent studies on making ViT more lightweight to minimize model complexity and increase efficiency. In this paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps, which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch ffner details for segmentation. We conduct experiments on various datasets, including iSeg-2017, Hippocampus and Cardiac to verify the performance and efficiency of 3D-EffiViTCaps, which performs better than previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We further implement a series of ablation experiments on the main blocks. Our code is available at: https://github.com/HidNeuron/3D-EffiViTCaps.

An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and images, has inspired researchers to delve into constructing SR from multi-modal information without using IDs. However, the complexity of multi-modal learning manifests in diverse feature extractors, fusion methods, and pre-trained models. Consequently, designing a simple and universal \textbf{M}ulti-\textbf{M}odal \textbf{S}equential \textbf{R}ecommendation (\textbf{MMSR}) framework remains a formidable challenge. We systematically summarize the existing multi-modal related SR methods and distill the essence into four core components: visual encoder, text encoder, multimodal fusion module, and sequential architecture. Along these dimensions, we dissect the model designs, and answer the following sub-questions: First, we explore how to construct MMSR from scratch, ensuring its performance either on par with or exceeds existing SR methods without complex techniques. Second, we examine if MMSR can benefit from existing multi-modal pre-training paradigms. Third, we assess MMSR's capability in tackling common challenges like cold start and domain transferring. Our experiment results across four real-world recommendation scenarios demonstrate the great potential ID-agnostic multi-modal sequential recommendation. Our framework can be found at: https://github.com/MMSR23/MMSR.

Look-Around Before You Leap: High-Frequency Injected Transformer for Image Restoration

Transformer-based approaches have achieved superior performance in image restoration, since they can model long-term dependencies well. However, the limitation in capturing local information restricts their capacity to remove degradations. While existing approaches attempt to mitigate this issue by incorporating convolutional operations, the core component in Transformer, i.e., self-attention, which serves as a low-pass filter, could unintentionally dilute or even eliminate the acquired local patterns. In this paper, we propose HIT, a simple yet effective High-frequency Injected Transformer for image restoration. Specifically, we design a window-wise injection module (WIM), which incorporates abundant high-frequency details into the feature map, to provide reliable references for restoring high-quality images. We also develop a bidirectional interaction module (BIM) to aggregate features at different scales using a mutually reinforced paradigm, resulting in spatially and contextually improved representations. In addition, we introduce a spatial enhancement unit (SEU) to preserve essential spatial relationships that may be lost due to the computations carried out across channel dimensions in the BIM. Extensive experiments on 9 tasks (real noise, real rain streak, raindrop, motion blur, moir\'e, shadow, snow, haze, and low-light condition) demonstrate that HIT with linear computational complexity performs favorably against the state-of-the-art methods. The source code and pre-trained models will be available at https://github.com/joshyZhou/HIT.

Seeing the Unseen: A Frequency Prompt Guided Transformer for Image Restoration

How to explore useful features from images as prompts to guide the deep image restoration models is an effective way to solve image restoration. In contrast to mining spatial relations within images as prompt, which leads to characteristics of different frequencies being neglected and further remaining subtle or undetectable artifacts in the restored image, we develop a Frequency Prompting image restoration method, dubbed FPro, which can effectively provide prompt components from a frequency perspective to guild the restoration model address these differences. Specifically, we first decompose input features into separate frequency parts via dynamically learned filters, where we introduce a gating mechanism for suppressing the less informative elements within the kernels. To propagate useful frequency information as prompt, we then propose a dual prompt block, consisting of a low-frequency prompt modulator (LPM) and a high-frequency prompt modulator (HPM), to handle signals from different bands respectively. Each modulator contains a generation process to incorporate prompting components into the extracted frequency maps, and a modulation part that modifies the prompt feature with the guidance of the decoder features. Experimental results on commonly used benchmarks have demonstrated the favorable performance of our pipeline against SOTA methods on 5 image restoration tasks, including deraining, deraindrop, demoir\'eing, deblurring, and dehazing. The source code and pre-trained models will be available at https://github.com/joshyZhou/FPro.

HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatial analysis. Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers. However, it avoids the associated high computational demands. HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis. This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers. HSIMamba were tested against three widely recognized datasets Houston 2013, Indian Pines, and Pavia University and demonstrated exceptional performance, surpassing existing state-of-the-art models in HSI classification. This method highlights the methodological innovation of HSIMamba and its practical implications, which are particularly valuable in contexts where computational resources are limited. HSIMamba redefines the standards of efficiency and accuracy in HSI classification, thereby enhancing the capabilities of remote sensing applications. Hyperspectral imaging has become a crucial tool for environmental surveillance, agriculture, and other critical areas that require detailed analysis of the Earth surface. Please see our code in HSIMamba for more details.