MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE

Geng Zhang, Yuxuan Han, Yuxuan Lou, Wangbo Zhao†, Yiqi Zhang, Yang You†

• MoNE achieves efficient MoE compression by replacing redundant experts with lightweight “novices,” significantly reducing memory overhead while maintaining higher accuracy than traditional pruning.

RAPID^3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer

Wangbo Zhao, Yizeng Han, Zhiwei Tang, Jiasheng Tang, Pengfei Zhou, Kai Wang, Bohan Zhuang, Zhangyang Wang, Fan Wang, Yang You

• RAPID3 speeds up DiT models like FLUX by 3x using adaptive reinforcement learning policies that accelerate sampling without needing any fine-tuning of the original generator.

DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation

Code

Wangbo Zhao*, Yizeng Han*, Jiasheng Tang, Kai Wang, Hao Luo, Yibing Song, Gao Huang, Fan Wang, Yang You

• DyDiT++ enhances DyDiT with integration with flow matching for broader applications, improved adaptability to complex tasks, and timestep-based dynamic LoRA for efficient training.

EA-ViT: Efficient Adaptation for Elastic Vision Transformer

Code

Chen Zhu, Wangbo Zhao†, Huiwen Zhang, Samir Khaki, Yuhao Zhou, Weidong Tang, Shuo Wang, Zhihang Yuan, Yuzhang Shang, Xiaojiang Peng, Kai Wang, Dawei Yang†

• EA-ViT is an efficient adaptation framework for Vision Transformers, enabling a single process to generate flexible models of varying sizes for diverse resource constraints, using a nested elastic architecture and a lightweight router optimized with Pareto-optimal configurations.

Unsupervised Learning for Class Distribution Mismatch

Pan Du, Wangbo Zhao†, Xinai Lu, Nian Liu, Zhikai Li, Chaoyu Gong, Suyun Zhao†, Hong Chen, Cuiping Li, Kai Wang, Yang You

• UCDM addresses Class Distribution Mismatch (CDM) by leveraging unlabeled data to train classifiers through positive-negative pairs, synthesized using a diffusion model, and a confidence-based pseudo-labeling mechanism, achieving superior performance over semi-supervised methods without relying on labeled data.

A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs

Code

Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, Yang You

• We employ the attention map aggregated from a small VLM to guide visual token pruning in a large VLM. And an early exiting mechanism is developed to fully use the small VLM’s predictions, dynamically invoking the larger VLM only when necessary, yielding a superior trade-off between accuracy and computation.

Dynamic diffusion transformer

Code

Wangbo Zhao, Yizeng Han, Jiasheng Tang, Kai Wang, Yibing Song, Gao Huang, Fan Wang, Yang You

• We propose to dynamically adjust the computation of DiT in different timesteps and spatial locations of images. The computation of DiT-XL could be saved by 50% without sacrificing generation quality.

Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation

Code

Wangbo Zhao, Jiasheng Tang, Yizeng Han, Yibing Song, Kai Wang, Gao Huang, Fan Wang, Yang You

• We propose to adapt static ViT to dynamic ViT via parameter-efficient fine-tuning without full-parameter tuning.

Mmbench: Is your multi-modal model an all-around player?

Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, Dahua Lin

• We propose MMBench, a bilingual benchmark for assessing the multi-modal capabilities of VLMs.

Modeling motion with multi-modal features for text-based video segmentation

Wangbo Zhao, Kai Wang, Xiangxiang Chu, Fuzhao Xue, Xinchao Wang, Yang You

• We design a method to fuse and align appearance, motion, and linguistic features to achieve accurate text-based video segmentation.

Light field saliency detection with dual local graph learning and reciprocative guidance

Nian Liu*, Wangbo Zhao*, Dingwen Zhang, Junwei Han, Ling Shao

• We introduce a reciprocative guidance scheme for light field saliency detection.

Weakly supervised video salient object detection

Wangbo Zhao, Jing Zhang, Long Li, Nick Barnes, Nian Liu, Junwei Han

• We present the first weakly supervised video salient object detection model based on relabeled fixation guided scribble annotations.