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Novel Object Detection

Novel Object Detection is a challenging task introduced by Fomenko et.al. in their paper "Learning to Discover and Detect Objects". The goal in this task is to measure mAP performance on known as well as novel classes, where the known classes correspond to the 80 COCO classes, and the novel classes are the remaining 1123 classes from LVIS dataset. Thus, during training the model can only be trained with annotations from COCO dataset, but during evaluation/inference it is expected to BOTH classify and detect objects belonging to ALL the classes in the LVIS dataset.

Papers

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Showing 1–25 of 53 papers

TitleStatusHype
Mamba YOLO: A Simple Baseline for Object Detection with State Space ModelCode4
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object DetectionCode2
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detectionCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object DetectionCode1
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite ImageryCode1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
Scaling Novel Object Detection with Weakly Supervised Detection TransformersCode1
Learning to Discover and Detect ObjectsCode1
Open-World Semi-Supervised LearningCode1
Universal-Prototype Enhancing for Few-Shot Object DetectionCode1
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
A Unified Objective for Novel Class DiscoveryCode1
Deep Watershed Detector for Music Object Recognition—0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection—0
Deep Regionlets for Object Detection—0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection—0
Any-Shot Object Detection—0
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving—0
Accurate Object Detection with Joint Classification-Regression Random Forests—0
An object detection approach for lane change and overtake detection from motion profiles—0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision—0
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules—0
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