Novel Object Detection
24 papers with code • 1 benchmarks • 1 datasets
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.
Benchmarks
These leaderboards are used to track progress in Novel Object Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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LVIS
Most implemented papers
Grid R-CNN
open-mmlab/mmdetection
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CVPR 2019
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Deploying deep learning models on embedded systems has been challenging due to limited computing resources.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
We present a novel object detection pipeline for localization and recognition in three dimensional environments.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
Learning to Detect and Retrieve Objects from Unlabeled Videos
In this work, we propose to exploit the natural correlation in narrations and the visual presence of objects in video, to learn an object detector and retrieval without any manual labeling involved.
Automatic Signboard Detection and Localization in Densely Populated Developing Cities
sadrultoaha/signboard-detection
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We have taken an incremental approach in reaching our final proposed method through detailed evaluation and comparison with baselines using our constructed SVSO (Street View Signboard Objects) signboard dataset containing signboard natural scene images of six developing countries.
Open-World Semi-Supervised Learning
snap-stanford/orca
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ICLR 2022
Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.
Universal-Prototype Enhancing for Few-Shot Object Detection
Thus, we develop a new framework of few-shot object detection with universal prototypes ({FSOD}^{up}) that owns the merit of feature generalization towards novel objects.
Instance Segmentation of Microscopic Foraminifera
The model achieves a (COCO-style) average precision of $0. 78 \pm 0. 00$ on the classification and detection task, and $0. 80 \pm 0. 00$ on the segmentation task.
