Papers Survey
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A Survey of Context Engineering for Large Language Models
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple …
RAGRetrievalRetrieval-augmented GenerationSurveyTransformer-based Spatial Grounding: A Comprehensive Survey
Spatial grounding, the process of associating natural language expressions with corresponding image regions, has rapidly advanced due to the introduction of transformer-based models, significantly enhancing multimodal re…
cross-modal alignmentSurveySystematic Literature ReviewA Survey of Deep Learning for Geometry Problem Solving
Geometry problem solving is a key area of mathematical reasoning, which is widely involved in many important fields such as education, mathematical ability assessment of artificial intelligence, and multimodal ability as…
Deep LearningGeometry Problem SolvingMathematical ReasoningSurveyA Survey on Interpretability in Visual Recognition
In recent years, visual recognition methods have advanced significantly, finding applications across diverse fields. While researchers seek to understand the mechanisms behind the success of these models, there is also a…
Autonomous DrivingSurveyPrompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
The Segment Anything Model (SAM) has revolutionized image segmentation through its innovative prompt-based approach, yet the critical role of prompt engineering in its success remains underexplored. This paper presents t…
Image SegmentationPrompt EngineeringSemantic SegmentationSurveyGR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance ov…
Recommendation SystemsSurveyExplainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey
Explainable artificial intelligence (XAI) has become increasingly important in biomedical image analysis to promote transparency, trust, and clinical adoption of DL models. While several surveys have reviewed XAI techniq…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)SurveyWhat Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
Advances in vision-based sensors and computer vision algorithms have significantly improved the analysis and understanding of traffic scenarios. To facilitate the use of these improvements for road safety, this survey sy…
Scene UnderstandingSurveyCritical Nodes Identification in Complex Networks: A Survey
Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critical nodes is a central theme in contempor…
Computational EfficiencySurveyA Survey on Prompt Tuning
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classify existing approaches into two categorie…
Computational EfficiencyMixture-of-ExpertsPrompt LearningSurvey+1A Survey on Latent Reasoning
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpreta…
SurveyHyperspectral Anomaly Detection Methods: A Survey and Comparative Study
Hyperspectral images are high-dimensional datasets comprising hundreds of contiguous spectral bands, enabling detailed analysis of materials and surfaces. Hyperspectral anomaly detection (HAD) refers to the technique of …
Anomaly DetectionBenchmarkingComputational EfficiencySurveyAdvancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques
Offline Handwritten Text Recognition (HTR) systems play a crucial role in applications such as historical document digitization, automatic form processing, and biometric authentication. However, their performance is ofte…
Data AugmentationHandwritten Text RecognitionHTRSurvey+1Motion Generation: A Survey of Generative Approaches and Benchmarks
Motion generation, the task of synthesizing realistic motion sequences from various conditioning inputs, has become a central problem in computer vision, computer graphics, and robotics, with applications ranging from an…
Motion GenerationSurveyVisual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always important field of vision-based hand gesture recognitio…
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionNavigate+1A Survey on Vision-Language-Action Models for Autonomous Driving
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single p…
Autonomous DrivingAutonomous VehiclesNatural Language UnderstandingSurvey+1Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the need for robust task generalization. Pr…
Feature EngineeringImage GenerationPrompt EngineeringSurveyPoint Cloud Compression and Objective Quality Assessment: A Survey
The rapid growth of 3D point cloud data, driven by applications in autonomous driving, robotics, and immersive environments, has led to criticals demand for efficient compression and quality assessment techniques. Unlike…
Autonomous DrivingBenchmarkingPoint Cloud Quality AssessmentSurveyA Survey of Continual Reinforcement Learning
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field due to the rapid development of deep ne…
Continual LearningDecision Makingreinforcement-learningReinforcement Learning+33D Shape Generation: A Survey
Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive o…
3D Shape GenerationDiversitySurvey