3 citations · 3 across the 22 of their papers we have counts for
14 papers · 1 filter
Fose: Fusion of One-Step Diffusion and End-to-End Network for Pansharpening
Kai Liu, Zeli Lin, Weibo Wang +2
Pansharpening is a significant image fusion task that fuses low-resolution multispectral images (LRMSI) and high-resolution panchromatic images (PAN) to obtain high-resolution mult…
UmniBench: Unified Understand and Generation Model Oriented Omni-dimensional Benchmark
Kai Liu, Leyang Chen, Wenbo Li +5
Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. However, evaluations of unified multimodal models (UMMs) rem…
InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
Ziqing Zhang, Kai Liu, Zheng Chen +5
Real-world videos often extend over thousands of frames. Existing generative video super-resolution (VSR) approaches, however, face two persistent challenges when processing long s…
CLQ: Cross-Layer Guided Orthogonal-based Quantization for Diffusion Transformers
Kai Liu, Shaoqiu Zhang, Linghe Kong +1
Visual generation quality has been greatly promoted with the rapid advances in diffusion transformers (DiTs), which is attributed to the scaling of model size and complexity. Howev…
Segment Concealed Objects with Incomplete Supervision
Chunming He, Kai Li, Yachao Zhang +8
Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotate…
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…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Xianglong Yan, Zhiteng Li, Tianao Zhang +4
Large language models (LLMs) have demonstrated remarkable performance, but their long-context reasoning remains constrained by the excessive memory required for the Key-Value (KV)…
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Zhiteng Li, Hanxuan Li, Junyi Wu +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…
QuantCache: Adaptive Importance-Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation
Junyi Wu, Zhiteng Li, Zheng Hui +3
Recently, Diffusion Transformers (DiTs) have emerged as a dominant architecture in video generation, surpassing U-Net-based models in terms of performance. However, the enhanced ca…
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li +5
Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration ability. However, accuracy degrad…
RUN: Reversible Unfolding Network for Concealed Object Segmentation
Chunming He, Rihan Zhang, Fengyang Xiao +7
Existing concealed object segmentation (COS) methods frequently utilize reversible strategies to address uncertain regions. However, these approaches are typically restricted to th…
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen +5
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and compu…
AdaSVD: Adaptive Singular Value Decomposition for Large Language Models
Zhiteng Li, Mingyuan Xia, Jingyuan Zhang +5
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP) tasks, yet their substantial memory requirements present significant challenges f…