Fall detection (FD) systems are an important part of healthcare monitoring, especially for elderly populations, where quick intervention can prevent serious injuries. This paper introduces an optimized YOLOV5-based framework that combines dual attention mechanisms for improved FD in real-time edge deployment situations. The proposed design integrates the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) blocks within the YOLOv5 backbone, along with an improved Focus module that uses slice-based feature extraction. These enhancements allow the model to effectively capture both spatial and channel-wise dependencies, which are essential for distinguishing fall events f... More >

The proliferation of deepfake technology poses significant threats to digital media authenticity, necessitating robust detection systems to combat manipulated content. This paper presents a novel attention-based framework for deepfake detection that systematically integrates multiple complementary attention mechanisms to enhance discriminative feature learning. Our approach combines spatial attention, multi-head self-attention, and channel attention modules with a VGG-16 backbone to capture comprehensive representations across different feature spaces. The spatial attention mechanism focuses on discriminative facial regions, while multi-head self-attention captures long-range spatial depende... More >

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, providing essential support for urban planning, traffic control, and congestion mitigation. To address the challenges of spatial heterogeneity and temporal dynamics inherent in traffic data, this paper proposes AST-GNNFormer, an adaptive spatio-temporal graph neural network that integrates graph attention mechanisms with temporal convolution. The model introduces three key components to enhance predictive accuracy and generalization: (1) a Layer-aware Information Preservation mechanism that mitigates over-smoothing in deep GNNs by retaining original node features across layers; (2) an Inter-Layer At... More >

Poetry, as a profound and creative form of human expression, presents unique challenges in interpretation and summarization due to its reliance on figurative language, symbolism, and deeper meanings. Building upon the PoemSum dataset, which introduced the task of poem summarization, we extend its scope by exploring multimodal applications. Specifically, we implement and fine-tune two state-of-the-art abstractive summarization models—BART and T5—to generate concise and meaningful interpretations of poems, focusing on figurative summarization that captures metaphorical and symbolic elements inherent in poetic language. These summaries are then transformed into visual representations using... More >

Accurate segmentation of colorectal cancer (CRC) from endoscopic images is crucial for computer-aided diagnosis. Visual intelligence enhances detection precision, supporting clinical decision-making. However, current segmentation methods often struggle with accurately delineating fine-grained lesion boundaries due to limited context comprehension and inadequate attention to optimal features. Additionally, the poor fusion of multi-scale semantic cues hinders performance, especially in complex endoscopic scenarios. To address these issues, we introduce ColoSegNet, a Visual Intelligence-Driven Triple Attention Feature Fusion Network designed for high-precision CRC segmentation. Our approach beg... More >