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20232026
most citedDiP-GO: A Diffusion Pruner via Few-step Gradient Optimization

4 citations · 5 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

DiffMagicFace: Identity Consistent Facial Editing of Real Videos

Huanghao Yin, Shenkun Xu, Kanle Shi +2

Text-conditioned image editing has greatly benefitted from the advancements in Image Diffusion Models. However, extending these techniques to facial video editing introduces challe…

cs.CV2026

DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity

Haowei Zhu, Ji Liu, Ziqiong Liu +4

Diffusion models demonstrate outstanding performance in image generation, but their multi-step inference mechanism requires immense computational cost. Previous works accelerate in…

cs.CV2026

Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting

Tingxuan Huang, Haowei Zhu, Jun-hai Yong +2

Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains a significant challenge. Existing Gaussian splatting approaches for dynamic scene…

cs.CV2026

TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Haowei Zhu, Tingxuan Huang, Xing Wang +7

Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (T…

cs.CV2026

DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation

Jiajun jiao, Haowei Zhu, Puyuan Yang +8

Diffusion models have achieved remarkable success in image and video generation. However, their inherently multiple step inference process imposes substantial computational overhea…

cs.CV2025

ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection

Haowei Zhu, Tianxiang Pan, Rui Qin +2

The scale and quality of datasets are crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative mod…

cs.CV2024

Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning

Haowei Zhu, Fangyuan Zhang, Rui Qin +3

As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance com…

cs.CV2024★ 4 cited

DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization

Haowei Zhu, Dehua Tang, Ji Liu +12

Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resou…

cs.CV2024★ 1 cited

Distribution-Aware Data Expansion with Diffusion Models

Haowei Zhu, Ling Yang, Jun-Hai Yong +5

The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeav…

cs.CV2023

Low-Confidence Samples Mining for Semi-supervised Object Detection

Guandu Liu, Fangyuan Zhang, Tianxiang Pan +1

Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo-labels with hig…