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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
PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region—0
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection—0
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving—0
Visual Understanding of Complex Table Structures from Document Images—0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data—0
Instance Segmentation of Microscopic ForaminiferaCode0
Oriented Bounding Boxes for Small and Freely Rotated Objects—0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision—0
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection—0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision—0
Any-Shot Object Detection—0
Automatic Signboard Detection and Localization in Densely Populated Developing CitiesCode0
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations—0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing ImageryCode0
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features—0
Grid R-CNNCode0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection—0
Object Detection based Deep Unsupervised Hashing—0
Deep Watershed Detector for Music Object Recognition—0
Deep Regionlets for Object Detection—0
CAD Priors for Accurate and Flexible Instance Reconstruction—0
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop ObjectsCode0
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection—0
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