4 citations · 5 across the 12 of their papers we have counts for
13 papers
StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Keming Wu, Baoyi Wang, Kaichen Zhang +7
Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compressi…
WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors
Keming Wu, Yijing Cui, Wenhan Xue +11
Commercial video generation systems such as Seedance2.0 and Veo3.1 have rapidly improved, strengthening the view that video generators may be evolving into "world simulators." Yet…
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…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Keming Wu, Zuhao Yang, Kaichen Zhang +24
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…
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…
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…
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…
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…
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling
Rui Qin, Qijie Wang, Ming Sun +3
Diffusion models have gained attention for their success in modeling complex distributions, achieving impressive perceptual quality in SR tasks. However, existing diffusion-based S…
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…
FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing
Zekai Li, Jintu Zheng, Ji Liu +9
Recently, large language models (LLMs) have demonstrated superior performance across various tasks by adhering to scaling laws, which significantly increase model size. However, th…
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…
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…