Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition

• URL: http://arxiv.org/abs/2409.06371v1
• Date: Tue, 10 Sep 2024 09:53:06 GMT
• Title: Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition
• Authors: Junzheng Zhang, Weijia Guo, Bochao Liu, Ruixin Shi, Yong Li, Shiming Ge,
• Abstract summary: Very low-resolution face recognition is challenging due to loss of informative facial details in resolution degradation. We propose a generative-discriminative representation distillation approach that combines generative representation with cross-resolution aligned knowledge distillation. Our approach improves the recovery of the missing details in very low-resolution faces and achieves better knowledge transfer.
• Score: 19.634712802639356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Very low-resolution face recognition is challenging due to the serious loss of informative facial details in resolution degradation. In this paper, we propose a generative-discriminative representation distillation approach that combines generative representation with cross-resolution aligned knowledge distillation. This approach facilitates very low-resolution face recognition by jointly distilling generative and discriminative models via two distillation modules. Firstly, the generative representation distillation takes the encoder of a diffusion model pretrained for face super-resolution as the generative teacher to supervise the learning of the student backbone via feature regression, and then freezes the student backbone. After that, the discriminative representation distillation further considers a pretrained face recognizer as the discriminative teacher to supervise the learning of the student head via cross-resolution relational contrastive distillation. In this way, the general backbone representation can be transformed into discriminative head representation, leading to a robust and discriminative student model for very low-resolution face recognition. Our approach improves the recovery of the missing details in very low-resolution faces and achieves better knowledge transfer. Extensive experiments on face datasets demonstrate that our approach enhances the recognition accuracy of very low-resolution faces, showcasing its effectiveness and adaptability.

Related papers

• Efficient Low-Resolution Face Recognition via Bridge Distillation [40.823152928253776]
  We propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory.
  arXiv  Detail & Related papers  (2024-09-18T08:10:35Z)
• Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition [30.568519905346253]
  We propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher's behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student.
  arXiv  Detail & Related papers  (2024-09-09T07:32:18Z)
• Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation [22.26932361388872]
  We propose a cross-resolution relational contrastive distillation approach to facilitate low-resolution object recognition. Our approach enables the student model to mimic the behavior of a well-trained teacher model. In this manner, the capability of recovering missing details of familiar low-resolution objects can be effectively enhanced.
  arXiv  Detail & Related papers  (2024-09-04T09:21:13Z)
• Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation [18.709870458307574]
  Low-resolution face recognition is a challenging task due to the missing of informative details. Recent approaches have proven that high-resolution clues can well guide low-resolution face recognition via proper knowledge transfer. We propose an adaptable instance-relation distillation approach to facilitate low-resolution face recognition.
  arXiv  Detail & Related papers  (2024-09-03T16:53:34Z)
• One Step Diffusion-based Super-Resolution with Time-Aware Distillation [60.262651082672235]
  Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. Recent techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. We propose a time-aware diffusion distillation method, named TAD-SR, to accomplish effective and efficient image super-resolution.
  arXiv  Detail & Related papers  (2024-08-14T11:47:22Z)
• Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors [56.82596340418697]
  We propose a simple yet effective framework comprising a pre-trained Stable Diffusion (SD) model containing rich generative priors, a unified head (U-head) capable of integrating hierarchical representations, and an adapted expert providing discriminative priors. Comprehensive investigations unveil potential characteristics of Vermouth, such as varying granularity of perception concealed in latent variables at distinct time steps and various U-net stages. The promising results demonstrate the potential of diffusion models as formidable learners, establishing their significance in furnishing informative and robust visual representations.
  arXiv  Detail & Related papers  (2024-01-29T10:36:57Z)
• Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation [12.090322373964124]
  Cross-resolution face recognition is a challenging problem for modern deep face recognition systems. This paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image.
  arXiv  Detail & Related papers  (2023-03-15T14:52:46Z)
• ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition [84.49978494275382]
  Knowledge Distillation (KD) refers to transferring knowledge from a large model to a smaller one. In this work, we focus on its application in face recognition. We propose a novel method named ProxylessKD that directly optimize face recognition accuracy.
  arXiv  Detail & Related papers  (2020-10-31T13:14:34Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

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.