collaborators

34 papers

cs.CV2026

TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion

Jiawei Guo, Junxian Li, Yixin Tang +4

Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi…

cs.CV2026

PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models

Yongsen Cheng, Kai Liu, Kaiwen Tao +5

Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challenging in resource-constrained sc…

cs.CV2026

GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction

Leyang Chen, Junyi Wu, Zhiteng Li +1

Streaming 3D reconstruction from long monocular video sequences requires maintaining a key-value (KV) cache that grows linearly with sequence length, creating a severe memory bottl…

cs.CV2026

Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution

Xun Zhang, Kaicheng Yang, Hongliang Lu +3

Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their heavy inference burden hinders rea…

cs.CV2026

GTR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

Junxian Li, Kai Liu, Zizhong Ding +4

The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on un…

cs.CV2026

PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow

Wei Dong, Han Zhou, Terry Ji +6

Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth res…