Semantic segmentation has undergone a remarkable transformation from traditional computer vision approaches to sophisticated deep learning architectures, culminating in the revolutionary capabilities introduced by foundation models. This comprehensive survey examines the technical progression of semantic segmentation methodologies, with particular emphasis on vision foundation models, such as the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP). This paper systematically analyzes how these large-scale pretrained models enable previously unattainable capabilities, including zero-shot learning and cross-domain generalization while identifying persistent challenges regarding computational efficiency and boundary precision. The investigation encompasses critical applications across medical imaging, remote sensing, and video understanding domains, revealing both transformative benefits and technical limitations. It concludes that foundation models represent a fundamental paradigm shift requiring hybrid approaches that effectively combine general capabilities with domain-specific optimizations.

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As urban traffic congestion continues to intensify, predicting short-term traffic flow has become essential to enabling real-time control in intelligent transportation systems (ITS). However, traditional models face significant limitations in capturing the spatiotemporal and nonlinear characteristics of traffic data. Long Short-Term Memory (LSTM) networks, with their gated mechanisms, can effectively model long-term dependencies and periodic patterns in traffic flow. The accuracy of these predictions directly influences decision-making in scenarios such as traffic guidance and emergency management, offering substantial practical value for improving road network efficiency. This study constructs an optimized LSTM model to evaluate its effectiveness in short-term traffic prediction and to compare predictive performance across different time granularities. A dual-layer LSTM architecture is employed, incorporating the Adam optimizer, Dropout, and early stopping as regularization strategies. Using urban traffic monitoring data from the United States, both hourly and daily prediction models are developed for experimental validation. Results indicate that the hourly prediction model (MSE = 0.0709) markedly surpasses the daily model (MSE = 0.2987), effectively identifying recurring patterns like rush periods in the morning and evening. These outcomes offer a practical solution for adaptive traffic regulation.

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This study proposes a novel malware detection framework integrating dynamic and static analysis, and realizes the collaborative processing of bi-modal data through a unified graph neural network architecture. Specifically: extracting the control flow and data dependency features from binary disassembly, and capturing the system call sequence with time attributes in the sandbox environment; After encoding the two types of features into heterogeneous relationship graphs, a two-branch network is adopted to process the static topology (graph convolutional layer) and dynamic sequence (graph attention layer) respectively; Finally, the classification decision-making is achieved by the feature fusion module. In the benchmark test set of EMBER, VirusShare, and CIC-MalMem, the accuracy rate of the framework exceeded 95%, which is 4 to 7 percentage points higher than the single-modal baseline. The recall rate of unknown malware families remained above 92%, and the single-sample detection time was less than 50 milliseconds. The ablation experiment confirmed that static features effectively resist shell confusion and dynamic temporal attributes improve the recognition of distorted viruses. The current system has limitations on anti-sandbox detection technology. Further research suggests combining reinforcement learning to dynamically adjust the sandbox depth and introducing contractive learning to optimize the discriminative ability of graph embedding.

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In video content analysis, accurate tracking and recognition of objects is a complex task. Current research has primarily focused on the development of complex scenes and fast-moving targets. Yet, there are challenges of small objects, long time-series dependencies, and object occlusion. In this paper, we propose the Intelli-context transformer to detect objects in a dynamic environment. Addressing this challenge, attention mechanisms, contextual information, and semantic information are integrated into Intelli-Context Transformer to enhance the accuracy of video object tracking. Intelli-Context Transformer employs an end-to-end training approach and incorporates a Contextual Spatiotemporal Attention Module, which dynamically adjusts the focus on different information to improve recognition accuracy. The proposed method is capable of capturing and analyzing the spatiotemporal features of a single target in videos in real time, effectively handling tracking tasks in complex scenes. Compared with state-of-the-art methods, Intelli-Context Transformer demonstrates its strong generalization capability in video object recognition. This research provides an efficient and reliable approach for dynamic target tracking in complex scenes and offers technical support for functions such as behavior analysis and anomaly detection, contributing to the development of intelligent video surveillance and navigation.

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