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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 26–50 of 53 papers

TitleStatusHype
Small Instance Detection by Integer Programming on Object Density Maps—0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision—0
Visual Understanding of Complex Table Structures from Document Images—0
Accurate Object Detection with Joint Classification-Regression Random Forests—0
WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images—0
An object detection approach for lane change and overtake detection from motion profiles—0
Any-Shot Object Detection—0
CAD Priors for Accurate and Flexible Instance Reconstruction—0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection—0
Deep Regionlets for Object Detection—0
Deep Watershed Detector for Music Object Recognition—0
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection—0
EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection—0
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules—0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision—0
Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images—0
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving—0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection—0
MASF-YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View—0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection—0
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations—0
Object Detection based Deep Unsupervised Hashing—0
Open-World Objectness Modeling Unifies Novel Object Detection—0
Oriented Bounding Boxes for Small and Freely Rotated Objects—0
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features—0
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