Multi-View Correlation Distillation for Incremental Object Detection

• URL: http://arxiv.org/abs/2107.01787v1
• Date: Mon, 5 Jul 2021 04:36:33 GMT
• Title: Multi-View Correlation Distillation for Incremental Object Detection
• Authors: Dongbao Yang, Yu Zhou and Weiping Wang
• Abstract summary: We propose a novel textbfMulti-textbfView textbfCorrelation textbfDistillation (MVCD) based incremental object detection method.
• Score: 12.536640582318949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real applications, new object classes often emerge after the detection model has been trained on a prepared dataset with fixed classes. Due to the storage burden and the privacy of old data, sometimes it is impractical to train the model from scratch with both old and new data. Fine-tuning the old model with only new data will lead to a well-known phenomenon of catastrophic forgetting, which severely degrades the performance of modern object detectors. In this paper, we propose a novel \textbf{M}ulti-\textbf{V}iew \textbf{C}orrelation \textbf{D}istillation (MVCD) based incremental object detection method, which explores the correlations in the feature space of the two-stage object detector (Faster R-CNN). To better transfer the knowledge learned from the old classes and maintain the ability to learn new classes, we design correlation distillation losses from channel-wise, point-wise and instance-wise views to regularize the learning of the incremental model. A new metric named Stability-Plasticity-mAP is proposed to better evaluate both the stability for old classes and the plasticity for new classes in incremental object detection. The extensive experiments conducted on VOC2007 and COCO demonstrate that MVCD can effectively learn to detect objects of new classes and mitigate the problem of catastrophic forgetting.

Related papers

• 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)
• I3DOD: Towards Incremental 3D Object Detection via Prompting [31.75287371048825]
  We present a novel Incremental 3D Object Detection framework with the guidance of prompting, i.e., I3DOD. Specifically, we propose a task-shared prompts mechanism to learn the matching relationships between the object localization information and category semantic information. Our method outperforms the state-of-the-art object detection methods by 0.6% - 2.7% in terms of mAP@0.25.
  arXiv  Detail & Related papers  (2023-08-24T02:54:38Z)
• 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)
• Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection [71.18882803642526]
  Deep learning approaches have shown remarkable performance in the 3D object detection task. They suffer from a catastrophic performance drop when incrementally learning new classes without revisiting the old data. This "catastrophic forgetting" phenomenon impedes the deployment of 3D object detection approaches in real-world scenarios. We present the first solution - SDCoT, a novel static-dynamic co-teaching method.
  arXiv  Detail & Related papers  (2021-12-14T09:03:41Z)
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [56.22467011292147]
  Several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. We propose the use of unlabeled in-the-wild data to bridge the non-occurrence caused by the missing base classes during the training of additional novel classes.
  arXiv  Detail & Related papers  (2021-10-28T10:57:25Z)
• Towards Generalized and Incremental Few-Shot Object Detection [9.033533653482529]
  A novel Incremental Few-Shot Object Detection (iFSOD) method is proposed to enable the effective continual learning from few-shot samples. Specifically, a Double-Branch Framework (DBF) is proposed to decouple the feature representation of base and novel (few-shot) class. We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection.
  arXiv  Detail & Related papers  (2021-09-23T12:38:09Z)
• Incrementally Zero-Shot Detection by an Extreme Value Analyzer [0.0]
  This paper introduces a novel strategy for both zero-shot learning and class-incremental learning in real-world object detection. We propose a novel extreme value analyzer to detect objects from old seen, new seen, and unseen classes, simultaneously. Experiments demonstrate the efficacy of our model in detecting objects from both the seen and unseen classes, outperforming the alternative models on Pascal VOC and MSCOCO datasets.
  arXiv  Detail & Related papers  (2021-03-23T15:06:30Z)
• Learning Adaptive Embedding Considering Incremental Class [55.21855842960139]
  Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: 1) Novel class detection. After the novel classes are detected, the model needs to be updated without re-training using entire previous data.
  arXiv  Detail & Related papers  (2020-08-31T04:11:24Z)
• Two-Level Residual Distillation based Triple Network for Incremental Object Detection [21.725878050355824]
  We propose a novel incremental object detector based on Faster R-CNN to continuously learn from new object classes without using old data. It is a triple network where an old model and a residual model as assistants for helping the incremental model learning on new classes without forgetting the previous learned knowledge.
  arXiv  Detail & Related papers  (2020-07-27T11:04:57Z)
• Incremental Object Detection via Meta-Learning [77.55310507917012]
  We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
  arXiv  Detail & Related papers  (2020-03-17T13:40:00Z)

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.