13 papers
Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement
Jue Gong, Zihan Zhou, Jingkai Wang +3
Face fill-light enhancement (FFE) brightens underexposed faces by adding virtual fill light while keeping the original scene illumination and background unchanged. Most face religh…
Asymmetric VAE for One-Step Video Super-Resolution Acceleration
Jianze Li, Yong Guo, Yulun Zhang +1
Diffusion models have significant advantages in the field of real-world video super-resolution and have demonstrated strong performance in past research. In recent diffusion-based…
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Yuanhao Cai, He Zhang, Xi Chen +11
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Anoth…
QuantFace: Efficient Quantization for Face Restoration
Jiatong Li, Libo Zhu, Haotong Qin +5
Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations hamper the widespread adoption of these models. In this work, we pr…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Xianglong Yan, Zhiteng Li, Tianao Zhang +4
Large language models (LLMs) have demonstrated remarkable performance, but their long-context reasoning remains constrained by the excessive memory required for the Key-Value (KV)…