Novel Object Detection
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Benchmarks
LVIS v1.0 val
Most implemented
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Grid R-CNN
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
Papers
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 Driving