SOTAVerified

Object Detection

Papers

Showing 751775 of 10957 papers

TitleStatusHype
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object DetectionCode1
QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space ModelCode1
SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary DetectionCode1
Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and FutureCode1
A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particlesCode1
OSSA: Unsupervised One-Shot Style AdaptationCode1
OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing ImagesCode1
A Confidence-Aware Matching Strategy For Generalized Multi-Object TrackingCode1
BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained DevicesCode1
Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image TranslationCode1
Neuromorphic Drone Detection: an Event-RGB Multimodal ApproachCode1
PDT: Uav Target Detection Dataset for Pests and Diseases TreeCode1
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary NoduleCode1
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object TrackingCode1
Towards Physically Realizable Adversarial Attacks in Embodied Vision NavigationCode1
GLCONet: Learning Multi-source Perception Representation for Camouflaged Object DetectionCode1
SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer NetworksCode1
When to Extract ReID Features: A Selective Approach for Improved Multiple Object TrackingCode1
LEROjD: Lidar Extended Radar-Only Object DetectionCode1
Can OOD Object Detectors Learn from Foundation Models?Code1
Visual Grounding with Multi-modal Conditional AdaptationCode1
SSFam: Scribble Supervised Salient Object Detection FamilyCode1
Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target DetectionCode1
LowFormer: Hardware Efficient Design for Convolutional Transformer BackbonesCode1
Latent Distillation for Continual Object Detection at the EdgeCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Co-DETRbox mAP66Unverified
2InternImage-H (M3I Pre-training)box mAP65.5Unverified
3M3I Pre-training (InternImage-H)box mAP65.4Unverified
4MoCaEbox mAP65.1Unverified
5Co-DETR (Swin-L)box mAP64.8Unverified
6Focal-Stable-DINO (Focal-Huge, no TTA)box mAP64.8Unverified
7EVAbox mAP64.7Unverified
8Group DETR v2box mAP64.5Unverified
9FocalNet-H (DINO)box mAP64.4Unverified
10InternImage-XLbox mAP64.3Unverified