Open-World Object Detection via Discriminative Class Prototype Learning

• URL: http://arxiv.org/abs/2302.11757v1
• Date: Thu, 23 Feb 2023 03:05:04 GMT
• Title: Open-World Object Detection via Discriminative Class Prototype Learning
• Authors: Jinan Yu, Liyan Ma, Zhenglin Li, Yan Peng, Shaorong Xie
• Abstract summary: Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discnative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via
• Score: 4.055884768256164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect objects seen during training while identifying unseen classes, and 2) incrementally learn the knowledge of the identified unknown objects when the corresponding annotations is available. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discriminative Class Prototype Learning, which consists of a Proposal Embedding Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine Similarity-based Classifier (CSC). All our proposed modules aim to learn the discriminative embeddings of known classes in the feature space to minimize the overlapping distributions of known and unknown classes, which is beneficial to differentiate known and unknown classes. Extensive experiments performed on PASCAL VOC and MS-COCO benchmark demonstrate the effectiveness of our proposed method.

Related papers

• OSAD: Open-Set Aircraft Detection in SAR Images [1.1060425537315088]
  Open-set detection aims to enable detectors trained on a closed set to detect all known objects and identify unknown objects in open-set environments. To address these challenges, a novel open-set aircraft detector for SAR images is proposed, named Open-Set Aircraft Detection (OSAD) It is equipped with three dedicated components: global context modeling (GCM), location quality-driven pseudo labeling generation (LPG), and prototype contrastive learning (PCL)
  arXiv  Detail & Related papers  (2024-11-03T15:06:14Z)
• Open-World Object Detection with Instance Representation Learning [1.8749305679160366]
  We propose a method to train an object detector that can both detect novel objects and extract semantically rich features in open-world conditions. Our method learns a robust and generalizable feature space, outperforming other OWOD-based feature extraction methods.
  arXiv  Detail & Related papers  (2024-09-24T13:13:34Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection [17.766859354014663]
  Open-world object detection requires a model trained from data on known objects to detect both known and unknown objects. We propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer. We show that our model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection.
  arXiv  Detail & Related papers  (2023-01-05T09:11:16Z)
• Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation [79.62038105814658]
  Universal Domain aims to transfer the knowledge between datasets by handling two shifts: domain-shift and categoryshift. Main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approach this problem by first training the target adapted known and then relying on the single threshold to distinguish unknown target samples.
  arXiv  Detail & Related papers  (2022-12-16T09:01:57Z)
• Open World DETR: Transformer based Open World Object Detection [60.64535309016623]
  We propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. We fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-06T13:39:30Z)
• Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning [25.935629339091697]
  Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. We present Openset RCNN to address the challenging OSOD. We show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.
  arXiv  Detail & Related papers  (2022-11-21T15:00:04Z)
• Class-Specific Semantic Reconstruction for Open Set Recognition [101.24781422480406]
  Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes. We propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of auto-encoder (AE) and prototype learning. Results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition.
  arXiv  Detail & Related papers  (2022-07-05T16:25:34Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.