2 citations · 2 across the 10 of their papers we have counts for
9 papers · 1 filter
Cross-Resolution Diffusion Models via Network Pruning
Jiaxuan Ren, Junhan Zhu, Huan Wang
Diffusion models have demonstrated impressive image synthesis performance, yet many UNet-based models are trained at certain fixed resolutions. Their quality tends to degrade when…
OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Junhan Zhu, Hesong Wang, Mingluo Su +2
Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to…
See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering
Junjie Wang, Yunhan Tang, Yijie Wang +4
Multimodal Large Language Models (MLLMs) have pushed the frontiers of Knowledge-Based Visual Question Answering (KBVQA), yet their reasoning is fundamentally bottlenecked by a reli…
Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models
Huanyu Wang, Jushi Kai, Haoli Bai +4
Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of vision tokens introduced by high-resolution image and video…
Autoregressive Image Generation with Randomized Parallel Decoding
Haopeng Li, Jinyue Yang, Guoqi Li +1
We introduce ARPG, a novel visual Autoregressive model that enables Randomized Parallel Generation, addressing the inherent limitations of conventional raster-order approaches, whi…
FreeBlend: Advancing Concept Blending with Staged Feedback-Driven Interpolation Diffusion
Yufan Zhou, Haoyu Shen, Huan Wang
Concept blending is a promising yet underexplored area in generative models. While recent approaches, such as embedding mixing and latent modification based on structural sketches,…
Dynamic Token Reduction during Generation for Vision Language Models
Xiaoyu Liang, Chaofeng Guan, Jiaying Lu +3
Vision-Language Models (VLMs) have achieved notable success in multimodal tasks but face practical limitations due to the quadratic complexity of decoder attention mechanisms and a…
LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field
Huan Wang, Feitong Tan, Ziqian Bai +9
Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video. Howev…
Neural Radiance Fields with Torch Units
Bingnan Ni, Huanyu Wang, Dongfeng Bai +4
Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction methods widely used in industrial applications. Although prevalent methods achieve considerable improvem…