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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

Showing 110 of 53 papers

TitleStatusHype
Mamba YOLO: A Simple Baseline for Object Detection with State Space ModelCode4
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detectionCode2
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object DetectionCode2
Learning to Discover and Detect ObjectsCode1
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object DetectionCode1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
A Unified Objective for Novel Class DiscoveryCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
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