Ruihao Gong
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    • PiLLM: Resource-efficient LLM Inference Using Workload Prediction
    • Post-Training Quantization for Video Matting
    • QVGen: Pushing the Limit of Quantized Video Generative Models
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    • MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
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    • A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
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    • Towards Unified INT8 Training for Convolutional Neural Network
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Post-Training Quantization for Video Matting

Jan 1, 2026·
Tianrui Zhu
,
Houyuan Chen
Ruihao Gong
Ruihao Gong
,
Michele Magno
,
Haotong Qin
,
Kai Zhang
· 0 min read
Cite URL
Type
Conference paper
Publication
International Conference on Learning Representations (ICLR)
Last updated on Jan 1, 2026
Ruihao Gong
Authors
Ruihao Gong
← PiLLM: Resource-efficient LLM Inference Using Workload Prediction Jan 1, 2026
QVGen: Pushing the Limit of Quantized Video Generative Models Jan 1, 2026 →

© 2026 Ruihao Gong. This work is licensed under CC BY NC ND 4.0