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NTU-2022Fall-DLCV

Deep Learning for Computer Vision 深度學習於電腦視覺 by Frank Wang 王鈺強

Surpassed strong baseline for all four assignments (Final grade: 97.22/100)

⭐Please consider starring this project if you find my code useful.⭐

Outline

For more details, refer to the reports.

  • HW1 Spec: Report
    • Image classification ← Pretrained BEiT v1
      • Accuracy: 0.9360
    • Image segmentation ← Pretrained Deeplab v3
      • mIoU: 0.7438
  • HW2 Spec: Report
    • Face generation with GAN ← SNGAN (DCGAN with spectral normalization)
      • FID: 25.986
      • Face recognition accuracy: 0.9110
    • Digit generation with diffusion model ← Ho et al. Classifier-Free Diffusion Guidance.
      • Digit classifier accuracy: 0.9990
    • Domain adversarial network on MNIST-M, SVHN, and USPS
      • M→S Accuracy: 0.4943
      • M→U Accuracy: 0.9025
  • HW3 Spec: Report
    • Zero-Shot image classification with CLIP ← CLIP L/14
      • Accuracy: 0.8124
    • Image captioning with pretrained encoder ← Pretrained DeiT v3 as encoder
      • CIDEr score: 0.9413
      • CLIP score: 0.7310
    • Attention map visualization for image captioning
  • HW4 Spec: Report
    • 3D novel view synthesis ← DVGO (voxelized NeRF)
      • PSNR: 35.6029
      • SSIM: 0.9769
    • Self-Supervised pretraining for image classification ← BYOL
      • Accuracy: 0.5985
      • Outperforms the supervised equivalent in both full fine-tuning and frozen backbone evaluation.
  • Final Project Spec -- Challenge 2: Poster
    • Long-tailed 3D point cloud semantic segmentation