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cvpr2023_acceptedpapers_classification's Introduction

CVPR2023_Acceptedpapers_Classification

Part 1: Learning Optical Expansion from Scale Matching ---- Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Informationn

Part 2: CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching ---- Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark

Part 3: Vision Transformers are Good Mask Auto-Labelers ---- ECON: Explicit Clothed humans Optimized via Normal integration

Part 4: Zero-shot Generative Model Adaptation via Image-specific Prompt Learning ---- ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

Part 5: Token Boosting for Robust Self-Supervised Visual Transformer Pre-training ---- Mask-guided Matting in the Wild

Neural Parameterization & Editing

Appearance Modeling & BRDFs

Faster training

voxel/sparse-voxel/octree-based method

hash-table/triplane-based method

Others

3D Shape Generative Models

Reconstruction/driven from Monocular video based-NeRF

Sparse views

Geometry-awared NeRF

Garment related

UDF

Other

  • NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images

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