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)

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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