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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 1120 of 53 papers

TitleStatusHype
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object DetectionCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
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
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection0
PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region0
Learning to Discover and Detect ObjectsCode1
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