DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection

• URL: http://arxiv.org/abs/2407.13147v1
• Date: Thu, 18 Jul 2024 04:19:14 GMT
• Title: DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection
• Authors: Zhourui Zhang, Jun Li, Zhijian Wu, Jifeng Shen, Jianhua Xu,
• Abstract summary: 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.
• Score: 6.371066478190595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, current mainstream feature masking distillation methods mainly function by reconstructing selectively masked regions of a student network from the feature maps of a teacher network. In these methods, attention mechanisms can help to identify spatially important regions and crucial object-aware channel clues, such that the reconstructed features are encoded with sufficient discriminative and representational power similar to teacher features. However, previous feature-masking distillation methods mainly address homogeneous knowledge distillation without fully taking into account the heterogeneous knowledge distillation scenario. In particular, the huge discrepancy between the teacher and the student frameworks within the heterogeneous distillation paradigm is detrimental to feature masking, leading to deteriorating reconstructed student features. In this study, a novel dual feature-masking heterogeneous distillation framework termed DFMSD is proposed for object detection. More specifically, a stage-wise adaptation learning module is incorporated into the dual feature-masking framework, and thus the student model can be progressively adapted to the teacher models for bridging the gap between heterogeneous networks. Furthermore, a masking enhancement strategy is combined with stage-wise learning such that object-aware masking regions are adaptively strengthened to improve feature-masking reconstruction. In addition, semantic alignment is performed at each Feature Pyramid Network (FPN) layer between the teacher and the student networks for generating consistent feature distributions. Our experiments for the object detection task demonstrate the promise of our approach, suggesting that DFMSD outperforms both the state-of-the-art heterogeneous and homogeneous distillation methods.

Related papers

• Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization [17.543208086457234]
  We introduce a novel approach known as Structural Teacher-Student Normality Learning (SNL) We evaluate our proposed approach on two anomaly detection datasets, MVTecAD and VisA. Our method surpasses the state-of-the-art distillation-based algorithms by a significant margin of 3.9% and 1.5% on MVTecAD and 1.2% and 2.5% on VisA.
  arXiv  Detail & Related papers  (2024-02-27T00:02:24Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning [52.72888626663642]
  Copy-move forgery detection aims at detecting duplicated regions in a suspected forged image. Deep learning based copy-move forgery detection methods are in the ascendant. We propose a Transformer-style copy-move forgery network named as CMFDFormer. We also provide a novel PCSD continual learning framework to help CMFDFormer handle new tasks.
  arXiv  Detail & Related papers  (2023-11-22T09:27:46Z)
• 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)
• AMD: Adaptive Masked Distillation for Object [8.668808292258706]
  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.
  arXiv  Detail & Related papers  (2023-01-31T10:32:13Z)
• ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection [75.68023968735523]
  Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions. We propose an innovative approach called Asymmetric Distillation Post-Segmentation (ADPS) Our ADPS employs an asymmetric distillation paradigm that takes distinct forms of the same image as the input of the teacher-student networks. We show that ADPS significantly improves Average Precision (AP) metric by 9% and 20% on the MVTec AD and KolektorSDD2 datasets.
  arXiv  Detail & Related papers  (2022-10-19T12:04:47Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching [82.71578668091914]
  This paper establishes a compact learning framework that embeds the classification and mask-refinement components into a unified deep model. We propose a novel alternative self-dual teaching (ASDT) mechanism to encourage high-quality knowledge interaction.
  arXiv  Detail & Related papers  (2021-12-17T11:56:56Z)
• Differentiable Feature Aggregation Search for Knowledge Distillation [47.94874193183427]
  We introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework. DFA is a two-stage Differentiable Feature Aggregation search method motivated by DARTS in neural architecture search. Experimental results show that DFA outperforms existing methods on CIFAR-100 and CINIC-10 datasets.
  arXiv  Detail & Related papers  (2020-08-02T15:42:29Z)

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.