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class Developer:
def __init__(self):
self.name = "PinJui"
self.role = "Computer Vision Researcher"
self.languages = ["Python", "C"]
self.frameworks = ["PyTorch", "OpenCV", "Reflex"]
self.focus = ["Facial Expression Recognition", "Object Detection", "Chest X-ray Classification"]
def say_hi(self):
print("Thanks for dropping by! Let's build something amazing together!")
me = Developer()
me.say_hi()
Cross-timezone deadline tracker built with Python & Reflex
Stack: Python Reflex Web App
⚡ Multi-timezone support • Real-time countdown • Auto-sync functionality
Feature Decomposition & Reconstruction Learning - PyTorch implementation (CVPR'21)
Stack: PyTorch Computer Vision Deep Learning
⚡ State-of-the-art FER • Novel decomposition approach • Extensive benchmarking
Published in: IEEE Transactions on Affective Computing
Year: 2023
Topic: Facial Expression Recognition, Causal Reasoning, Deep Learning
Abstract: This work introduces a learnable counterfactual attention mechanism that uses causal reasoning to optimize feature discrimination and diversity, mitigating spurious correlations in facial expression recognition datasets.
Key Contributions:
- ✨ First work studying spurious correlations in FER from counterfactual perspective
- 🔬 Novel learnable counterfactual attention mechanism
- 📊 Extensive experiments on synthetic and four public datasets
- 🚀 Superior performance over existing methods
@ARTICLE{10243549,
author={Huang, Pin-Jui and Xie, Hongxia and Huang, Hung-Cheng and Shuai, Hong-Han and Cheng, Wen-Huang},
journal={IEEE Transactions on Affective Computing},
title={CA-FER: Mitigating Spurious Correlation With Counterfactual Attention in Facial Expression Recognition},
year={2024},
volume={15},
number={3},
pages={977-989},
keywords={Feature extraction;Correlation;Face recognition;Training;Computational modeling;Image color analysis;Deep learning;Causal learning;facial expression recognition;spurious correlation},
doi={10.1109/TAFFC.2023.3312768}}
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