AMD: Adaptive Masked Distillation for Object

• URL: http://arxiv.org/abs/2301.13538v1
• Date: Tue, 31 Jan 2023 10:32:13 GMT
• Title: AMD: Adaptive Masked Distillation for Object
• Authors: Guang Yang and Yin Tang and Jun Li and Jianhua Xu and Xili Wan
• Abstract summary: We propose a spatial-channel adaptive masked distillation (AMD) network for object detection. We employ a simple and efficient module to allow the student network channel to be adaptive. With the help of our proposed distillation method, the student networks report 41.3%, 42.4%, and 42.7% mAP scores.
• Score: 8.668808292258706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a general model compression paradigm, feature-based knowledge distillation allows the student model to learn expressive features from the teacher counterpart. In this paper, we mainly focus on designing an effective feature-distillation framework and propose a spatial-channel adaptive masked distillation (AMD) network for object detection. More specifically, in order to accurately reconstruct important feature regions, we first perform attention-guided feature masking on the feature map of the student network, such that we can identify the important features via spatially adaptive feature masking instead of random masking in the previous methods. In addition, we employ a simple and efficient module to allow the student network channel to be adaptive, improving its model capability in object perception and detection. In contrast to the previous methods, more crucial object-aware features can be reconstructed and learned from the proposed network, which is conducive to accurate object detection. The empirical experiments demonstrate the superiority of our method: with the help of our proposed distillation method, the student networks report 41.3\%, 42.4\%, and 42.7\% mAP scores when RetinaNet, Cascade Mask-RCNN and RepPoints are respectively used as the teacher framework for object detection, which outperforms the previous state-of-the-art distillation methods including FGD and MGD.

Related papers

• DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection [6.371066478190595]
  A novel dual feature-masking heterogeneous distillation framework termed DFMSD is proposed for object detection. A masking enhancement strategy is combined with stage-wise learning to improve feature-masking reconstruction. Experiments for the object detection task demonstrate the promise of our approach.
  arXiv  Detail & Related papers  (2024-07-18T04:19:14Z)
• Generative Model-based Feature Knowledge Distillation for Action Recognition [11.31068233536815]
  Our paper introduces an innovative knowledge distillation framework, with the generative model for training a lightweight student model. The efficacy of our approach is demonstrated through comprehensive experiments on diverse popular datasets.
  arXiv  Detail & Related papers  (2023-12-14T03:55:29Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Object-centric Cross-modal Feature Distillation for Event-based Object Detection [87.50272918262361]
  RGB detectors still outperform event-based detectors due to sparsity of the event data and missing visual details. We develop a novel knowledge distillation approach to shrink the performance gap between these two modalities. We show that object-centric distillation allows to significantly improve the performance of the event-based student object detector.
  arXiv  Detail & Related papers  (2023-11-09T16:33:08Z)
• Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction [6.798515070856465]
  We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD) Motivated by the effectiveness of Masked Image Modeling (MIM), we propose Masked Bounding Box Reconstruction (MBBR)
  arXiv  Detail & Related papers  (2023-11-08T16:59:26Z)
• DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation [10.437237606721222]
  We devise a Dual Masked Knowledge Distillation (DMKD) framework which can capture both spatially important and channel-wise informative clues. Our experiments on object detection task demonstrate that the student networks achieve performance gains of 4.1% and 4.3% with the help of our method.
  arXiv  Detail & Related papers  (2023-09-06T05:08:51Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation [35.233203757760066]
  We propose a learnable nonlinear channel-wise transformation to align the features of the student and the teacher model. Our method achieves significant performance improvements in various computer vision tasks.
  arXiv  Detail & Related papers  (2023-03-23T12:13:29Z)
• Exploring Target Representations for Masked Autoencoders [78.57196600585462]
  We show that a careful choice of the target representation is unnecessary for learning good representations. We propose a multi-stage masked distillation pipeline and use a randomly model as the teacher. A proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins.
  arXiv  Detail & Related papers  (2022-09-08T16:55:19Z)
• Aligning Pretraining for Detection via Object-Level Contrastive Learning [57.845286545603415]
  Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
  arXiv  Detail & Related papers  (2021-06-04T17:59:52Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)

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.