YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery

• URL: http://arxiv.org/abs/2404.00257v2
• Date: Mon, 22 Apr 2024 14:38:25 GMT
• Title: YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery
• Authors: Qian Wan, Xiang Xiang, Qinhao Zhou,
• Abstract summary: Open-world object detection (OWOD) has gotten a lot of attention recently. Previous approaches hinge on strongly-supervised or weakly-supervised novel-class data for novel-class detection. We construct a new benchmark that novel classes are only encountered at the inference stage.
• Score: 9.437644584141822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently. The challenge is how can a model detect novel classes and then incrementally learn them without forgetting previously known classes. Previous approaches hinge on strongly-supervised or weakly-supervised novel-class data for novel-class detection, which may not apply to real applications. We construct a new benchmark that novel classes are only encountered at the inference stage. And we propose a new OWOD detector YOLOOC, based on the YOLO architecture yet for the Open-Class setup. We introduce label smoothing to prevent the detector from over-confidently mapping novel classes to known classes and to discover novel classes. Extensive experiments conducted on our more realistic setup demonstrate the effectiveness of our method for discovering novel classes in our new benchmark.

Related papers

• Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization [63.66349334291372]
  We propose a framework with Meta prompt and Instance Contrastive learning (MIC) schemes. Firstly, we simulate a novel-class-emerging scenario to help the prompt that learns class and background prompts generalize to novel classes. Secondly, we design an instance-level contrastive strategy to promote intra-class compactness and inter-class separation, which benefits generalization of the detector to novel class objects.
  arXiv  Detail & Related papers  (2024-03-14T14:25:10Z)
• PANDAS: Prototype-based Novel Class Discovery and Detection [28.615935639878593]
  We look at ways to extend a detector trained for a set of base classes so it can spot the presence of novel classes. We propose PANDAS, a method for novel class discovery and detection. We experimentally demonstrate the effectiveness of PANDAS on the VOC 2012 and COCO-to-LVIS benchmarks.
  arXiv  Detail & Related papers  (2024-02-27T11:23:39Z)
• DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection [53.8061502411777]
  In real-world applications, an object detector often encounters object instances from new classes and needs to accommodate them effectively. Previous work formulated this critical problem as incremental object detection (IOD), which assumes the object instances of new classes to be fully annotated in incremental data. We consider a more realistic setting named semi-supervised IOD (SSIOD), where the object detector needs to learn new classes incrementally from a few labelled data and massive unlabelled data.
  arXiv  Detail & Related papers  (2023-12-13T10:46:14Z)
• ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection [7.122652901894367]
  Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set. We present a novel, yet simple technique that helps generalization on the overall distribution of novel classes.
  arXiv  Detail & Related papers  (2023-12-12T13:45:56Z)
• MetaGCD: Learning to Continually Learn in Generalized Category Discovery [26.732455383707798]
  We consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data. The goal is to continually discover novel classes while maintaining the performance in known classes. We propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting.
  arXiv  Detail & Related papers  (2023-08-21T22:16:49Z)
• Class-relation Knowledge Distillation for Novel Class Discovery [16.461242381109276]
  Key challenge lies in transferring the knowledge in the known-class data to the learning of novel classes. We introduce a class relation representation for the novel classes based on the predicted class distribution of a model trained on known classes. We propose a novel knowledge distillation framework, which utilizes our class-relation representation to regularize the learning of novel classes.
  arXiv  Detail & Related papers  (2023-07-18T11:35:57Z)
• Novel Class Discovery without Forgetting [72.52222295216062]
  We identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting. We propose a machine learning model to incrementally discover novel categories of instances from unlabeled data. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery.
  arXiv  Detail & Related papers  (2022-07-21T17:54:36Z)
• Class-incremental Novel Class Discovery [76.35226130521758]
  We study the new task of class-incremental Novel Class Discovery (class-iNCD) We propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-07-18T13:49:27Z)
• Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning [60.64535309016623]
  We propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision. We further introduce a incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-05-09T05:08:08Z)
• Adversarially Robust One-class Novelty Detection [83.1570537254877]
  We show that existing novelty detectors are susceptible to adversarial examples. We propose a defense strategy that manipulates the latent space of novelty detectors to improve the robustness against adversarial examples.
  arXiv  Detail & Related papers  (2021-08-25T10:41:29Z)

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.