SOTAVerified

Object Detection

Papers

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Showing 851–875 of 10957 papers

TitleStatusHype
On Moving Object Segmentation from Monocular Video with Transformers—0
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events—0
ROICtrl: Boosting Instance Control for Visual Generation—0
Deep Fourier-embedded Network for Bi-modal Salient Object DetectionCode1
RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos—0
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks—0
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel ObjectsCode1
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models—0
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learningCode0
Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking—0
Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and ImplementationCode1
TinyViM: Frequency Decoupling for Tiny Hybrid Vision MambaCode2
Open Vocabulary Monocular 3D Object DetectionCode2
Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment—0
Online Episodic Memory Visual Query Localization with Egocentric Streaming Object Memory—0
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation TrainingCode2
Leverage Task Context for Object Affordance Ranking—0
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation—0
Interpreting Object-level Foundation Models via Visual Precision SearchCode2
Machine Learning for the Digital Typhoon Dataset: Extensions to Multiple Basins and New Developments in Representations and TasksCode1
Imperceptible Adversarial Examples in the Physical World—0
CIA: Controllable Image Augmentation Framework Based on Stable DiffusionCode0
Diagnosis of diabetic retinopathy using machine learning & deep learning technique—0
Learn from Foundation Model: Fruit Detection Model without Manual AnnotationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Co-DETRbox mAP66—Unverified
2InternImage-H (M3I Pre-training)box mAP65.5—Unverified
3M3I Pre-training (InternImage-H)box mAP65.4—Unverified
4MoCaEbox mAP65.1—Unverified
5Co-DETR (Swin-L)box mAP64.8—Unverified
6Focal-Stable-DINO (Focal-Huge, no TTA)box mAP64.8—Unverified
7EVAbox mAP64.7—Unverified
8Group DETR v2box mAP64.5—Unverified
9FocalNet-H (DINO)box mAP64.4—Unverified
10InternImage-XLbox mAP64.3—Unverified