Papers Novel Object Detection
“Novel Object Detection” 태그가 달린 논문 59편 · 필터 해제
Privacy-Preserving Object Detection for Vision Transformer-Based Models
We propose a novel object detection method that enables us to protect sensitive visual information of test images. Previous studies considering visual information protection focus on image classification tasks. This pape…
Novel Object DetectionImage ClassificationDomain AdaptationHippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling
This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framework based on biological hippocampal memory modeling. This framework in…
Few-Shot Image ClassificationNovel Object DetectionImage RestorationA novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent years, Unmanned Aerial Vehicles (UAVs) …
Hyperparameter OptimizationNovel Object DetectionFew-Shot Incremental 3D Object Detection in Dynamic Indoor Environments
Incremental 3D object perception is a critical step toward embodied intelligence in dynamic indoor environments. However, existing incremental 3D detection methods rely on extensive annotations of novel classes for satis…
Novel Object Detection3D Object DetectionScalable Object Detection in the Car Interior With Vision Foundation Models
AI tasks in the car interior like identifying and localizing externally introduced objects is crucial for response quality of personal assistants. However, computational resources of on-board systems remain highly constr…
Novel Object DetectionScene UnderstandingButter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detection
Hierarchical feature representations play a pivotal role in computer vision, particularly in object detection for autonomous driving. Multi-level semantic understanding is crucial for accurately identifying pedestrians, …
Computational EfficiencyNovel Object DetectionAutonomous DrivingMambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection
Real-time object detection is a fundamental but challenging task in computer vision, particularly when computational resources are limited. Although YOLO-series models have set strong benchmarks by balancing speed and ac…
MambaNovel Object DetectionObjectobject-detection+3Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images
Despite recent advancements in computer vision research, object detection in aerial images still suffers from several challenges. One primary challenge to be mitigated is the presence of multiple types of variation in ae…
Novel Object DetectionObjectobject-detectionObject Detection+1MASF-YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View
With the rapid advancement of Unmanned Aerial Vehicle (UAV) and computer vision technologies, object detection from UAV perspectives has emerged as a prominent research area. However, challenges for detection brought by …
Novel Object Detectionobject-detectionObject DetectionSmall Object DetectionAn object detection approach for lane change and overtake detection from motion profiles
In the application domain of fleet management and driver monitoring, it is very challenging to obtain relevant driving events and activities from dashcam footage while minimizing the amount of information stored and anal…
Novel Object Detectionobject-detectionObject DetectionOpen-World Objectness Modeling Unifies Novel Object Detection
The challenge in open-world object detection, similarly to few- and zero-shot learning, is to generalize beyond the class distribution of the training data. In this paper, we propose a general class-agnostic objectne…
Novel Object Detectionobject-detectionObject DetectionOpen-vocabulary object detection+3WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection…
Novel Object Detectionobject-detectionObject DetectionFA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules
Over the past few years, the YOLO series of models has emerged as one of the dominant methodologies in the realm of object detection. Many studies have advanced these baseline models by modifying their architectures, enh…
feature selectionNovel Object Detectionobject-detectionObject DetectionEMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection
The detection of small objects, particularly traffic signs, is a critical subtask within object detection and autonomous driving. Despite the notable achievements in previous research, two primary challenges persist. Fir…
Autonomous DrivingNovel Object Detectionobject-detectionObject Detection+1Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection
Due to the effective performance of multi-scale feature fusion, Path Aggregation FPN (PAFPN) is widely employed in YOLO detectors. However, it cannot efficiently and adaptively integrate high-level semantic information w…
Novel Object Detectionobject-detectionObject DetectionReal-Time Object DetectionMamba YOLO: A Simple Baseline for Object Detection with State Space Model
Driven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution i…
GPUMambaNovel Object DetectionObject+3Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
In this paper we present YOLOX-ViT, a novel object detection model, and investigate the efficacy of knowledge distillation for model size reduction without sacrificing performance. Focused on underwater robotics, our res…
Knowledge DistillationNovel Object DetectionObjectobject-detection+1Fine-Grained Prototypes Distillation for Few-Shot Object Detection
Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. It attracts great concerns recently due to the practical meanings. Meta-learning has bee…
Few-Shot Object DetectionMeta-LearningNovel Object DetectionObject+2Enhancing Novel Object Detection via Cooperative Foundational Models
In this work, we address the challenging and emergent problem of novel object detection (NOD), focusing on the accurate detection of both known and novel object categories during inference. Traditional object detection a…
Novel Class DiscoveryNovel Object DetectionObjectobject-detection+2Beyond the Benchmark: Detecting Diverse Anomalies in Videos
Video Anomaly Detection (VAD) plays a crucial role in modern surveillance systems, aiming to identify various anomalies in real-world situations. However, current benchmark datasets predominantly emphasize simple, single…
Action RecognitionAnomaly DetectionDensity EstimationNovel Object Detection+4