SOTAVerified

Object Detection

Papers

Showing 11011150 of 10957 papers

TitleStatusHype
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing ImagesCode1
Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object DetectionCode1
InstructDET: Diversifying Referring Object Detection with Generalized InstructionsCode1
HOD: A Benchmark Dataset for Harmful Object DetectionCode1
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food DetectionCode1
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged InformationCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Collaborative Camouflaged Object Detection: A Large-Scale Dataset and BenchmarkCode1
WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object DetectionCode1
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height ComplementarityCode1
GET: Group Event Transformer for Event-Based VisionCode1
ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision TransformerCode1
DARTH: Holistic Test-time Adaptation for Multiple Object TrackingCode1
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object DetectionCode1
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature AugmentationCode1
Text-image Alignment for Diffusion-based PerceptionCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree FarmsCode1
MoCaE: Mixture of Calibrated Experts Significantly Improves Object DetectionCode1
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacherCode1
UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor ModalitiesCode1
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object DatasetCode1
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor DetectionCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
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