Yuhang Li

papers

569

total citations

papers (18)

Once Quantization-Aware Training: High Performance Extremely Low-Bit Architecture Search

ICCV 2021arXiv

citations

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

NEURIPS 2022arXiv

citations

Spiking Transformer with Spatial-Temporal Attention

CVPR 2025arXiv

citations

One-stage Prompt-based Continual Learning

ECCV 2024arXiv

citations

A Simple Background Augmentation Method for Object Detection with Diffusion Model

ECCV 2024arXiv

citations

DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding

ECCV 2024arXiv

citations

GenQ: Quantization in Low Data Regimes with Generative Synthetic Data

ECCV 2024arXiv

citations

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts

NEURIPS 2025arXiv

citations

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs

NEURIPS 2025arXiv

citations

SEENN: Towards Temporal Spiking Early Exit Neural Networks

NEURIPS 2023

citations

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks

NEURIPS 2021

citations

Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels

ICCV 2025

citations

NATRA: Noise-Agnostic Framework for Trajectory Prediction with Noisy Observations

ICCV 2025

citations

Yuhang Li

papers (18)

Neural Architecture Search for Spiking Neural Networks

Diversifying Sample Generation for Accurate Data-Free Quantization

Neuromorphic Data Augmentation for Training Spiking Neural Networks

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing

Once Quantization-Aware Training: High Performance Extremely Low-Bit Architecture Search

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Spiking Transformer with Spatial-Temporal Attention

One-stage Prompt-based Continual Learning

A Simple Background Augmentation Method for Object Detection with Diffusion Model

DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding

GenQ: Quantization in Low Data Regimes with Generative Synthetic Data

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs

SEENN: Towards Temporal Spiking Early Exit Neural Networks

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks

Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels

NATRA: Noise-Agnostic Framework for Trajectory Prediction with Noisy Observations

papers (18)

Neural Architecture Search for Spiking Neural Networks

Diversifying Sample Generation for Accurate Data-Free Quantization

Neuromorphic Data Augmentation for Training Spiking Neural Networks

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing

Once Quantization-Aware Training: High Performance Extremely Low-Bit Architecture Search

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Spiking Transformer with Spatial-Temporal Attention

One-stage Prompt-based Continual Learning

A Simple Background Augmentation Method for Object Detection with Diffusion Model

DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding

GenQ: Quantization in Low Data Regimes with Generative Synthetic Data

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs

SEENN: Towards Temporal Spiking Early Exit Neural Networks

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks

Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels

NATRA: Noise-Agnostic Framework for Trajectory Prediction with Noisy Observations