SOTAVerified

Object Detection

Papers

Showing 11261150 of 10957 papers

TitleStatusHype
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth CluesCode1
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object DetectionCode1
DAC-DETR: Divide the Attention Layers and ConquerCode1
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel ObjectsCode1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
M^3Net: Multilevel, Mixed and Multistage Attention Network for Salient Object DetectionCode1
Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation SurveillanceCode1
Salient Object Detection in Optical Remote Sensing Images Driven by TransformerCode1
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object UnderstandingCode1
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme ConditionsCode1
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object DetectionCode1
Beyond Generation: Harnessing Text to Image Models for Object Detection and SegmentationCode1
Zero-Shot Co-salient Object Detection FrameworkCode1
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-ArtCode1
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU MatchingCode1
Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNNCode1
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object DetectionCode1
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and MappingCode1
Object-Centric Multiple Object TrackingCode1
Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision modelsCode1
Unsupervised Recognition of Unknown Objects for Open-World Object DetectionCode1
CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and AttentionCode1
On the Robustness of Object Detection Models on Aerial ImagesCode1
Learning to Upsample by Learning to SampleCode1
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object DetectionCode1
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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