9 citations · 11 across the 22 of their papers we have counts for
17 papers · 1 filter
SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models
Kewei Zhang, Zheng Chen, Haotong Qin +1
Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large la…
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
Haotong Qin, Xudong Ma, Xianglong Liu +4
Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR m…
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
Yi Zhong, Haotong Qin, Xindong Zhang +2
Low-bit post-training quantization (PTQ) is a pivotal technique for deploying Vision-Language Models (VLMs) on resource-constrained devices. However, existing PTQ methods often deg…
Fast-SAM3D: 3Dfy Anything in Images but Faster
Weilun Feng, Mingqiang Wu, Zhiliang Chen +10
SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this work, we conduct the \textbf{firs…
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…
TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search
Kaicheng Yang, Kaisen Yang, Baiting Wu +5
Diffusion Transformers (DiTs) have emerged as a highly scalable and effective backbone for image generation, outperforming U-Net architectures in both scalability and performance.…
QuantSparse: Comprehensively Compressing Video Diffusion Transformer with Model Quantization and Attention Sparsification
Weilun Feng, Chuanguang Yang, Haotong Qin +8
Diffusion transformers exhibit remarkable video generation capability, yet their prohibitive computational and memory costs hinder practical deployment. Model quantization and atte…
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Kaicheng Yang, Xun Zhang, Haotong Qin +4
Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. Howeve…
FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing
Junyi Wu, Zhiteng Li, Haotong Qin +2
Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized i…
QuantFace: Efficient Quantization for Face Restoration
Jiatong Li, Libo Zhu, Haotong Qin +5
Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations hamper the widespread adoption of these models. In this work, we pr…
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Haotong Qin, Kaicheng Yang +5
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Jianze Li, Haotong Qin +4
Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced…
BiDense: Binarization for Dense Prediction
Rui Yin, Haotong Qin, Yulun Zhang +5
Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In th…
ARB-LLM: Alternating Refined Binarizations for Large Language Models
Zhiteng Li, Xianglong Yan, Tianao Zhang +7
Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. B…
Binarized Diffusion Model for Image Super-Resolution
Zheng Chen, Haotong Qin, Yong Guo +4
Advanced diffusion models (DMs) perform impressively in image super-resolution (SR), but the high memory and computational costs hinder their deployment. Binarization, an ultra-com…
Image Fusion via Vision-Language Model
Zixiang Zhao, Lilun Deng, Haowen Bai +8
Image fusion integrates essential information from multiple images into a single composite, enhancing structures, textures, and refining imperfections. Existing methods predominant…
BinaryHPE: 3D Human Pose and Shape Estimation via Binarization
Zhiteng Li, Yulun Zhang, Jing Lin +5
3D human pose and shape estimation (HPE) aims to reconstruct the 3D human body, face, and hands from a single image. Although powerful deep learning models have achieved accurate e…