1 citations · 3 across the 19 of their papers we have counts for
20 papers
CORAM: Coherent Orthogonal Rotation for Model Merging
Xinyi Sui, Ziran Liu, Nam Ling +2
Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithmetic in Euclidean weight spac…
CORA: Per-Slice Coherent Orthogonal Rotation for SVD-based Low-Rank Adaptation
Pengcheng Wang, Ziran Liu, Wei Wang +1
Parameter-Efficient Fine-Tuning (PEFT) commonly adapts pretrained weights through low-rank updates, and recent methods further exploit the singular value decomposition (SVD) of the…
Geometric and Spectral Alignment for Deep Neural Network II
Ziran Liu, Wei Wang, Jinhao Wang +5
This paper develops the angular and static-channel component of Geometric and Spectral Alignment for residual Jacobian chains. Starting from Cartan-coordinate rigidity and fitted e…
Geometric and Spectral Alignment for Deep Neural Network I
Ziran Liu, Wei Wang, Jinhao Wang +5
Deep residual architectures are modeled as products of near-identity Jacobians. This paper proves deterministic quotient-geometric estimates for singular spectra of Frobenius-norma…
Analysis of Converged 3D Gaussian Splatting Solutions: Density Effects and Prediction Limit
Zhendong Wang, Cihan Ruan, Jingchuan Xiao +5
We investigate what structure emerges in 3D Gaussian Splatting (3DGS) solutions from standard multi-view optimization. We term these Rendering-Optimal References (RORs) and analyze…
Efficient Post-Training Pruning of Large Language Models with Statistical Correction
Peiqi Yu, Jinhao Wang, Xinyi Sui +3
Post-training pruning is an effective approach for reducing the size and inference cost of large language models (LLMs), but existing methods often face a trade-off between pruning…
SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
Cihan Ruan, Lebin Zhou, Rongduo Han +6
DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy cons…
: A Large-Scale Multi-Modality Multi-View Synthesized Benchmark Dataset for Image Restoration and Content Creation
Yuanzhi Li, Lebin Zhou, Nam Ling +3
The gaming and entertainment industry is rapidly evolving, driven by immersive experiences and the integration of generative AI (GAI) technologies. Training such models effectively…
Collaborative Compression for Large-Scale MoE Deployment on Edge
Yixiao Chen, Yanyue Xie, Ruining Yang +6
The Mixture of Experts (MoE) architecture is an important method for scaling Large Language Models (LLMs). It increases model capacity while keeping computation cost low. However,…
ISCS: Parameter-Guided Feature Pruning for Resource-Constrained Embodied Perception
Jinhao Wang, Nam Ling, Wei Wang +1
Prior studies in embodied AI consistently show that robust perception is critical for human-robot interaction, yet deploying high-fidelity visual models on resource-constrained age…
4DGS-CC: A Contextual Coding Framework for 4D Gaussian Splatting Data Compression
Zicong Chen, Zhenghao Chen, Wei Jiang +3
Storage is a significant challenge in reconstructing dynamic scenes with 4D Gaussian Splatting (4DGS) data. In this work, we introduce 4DGS-CC, a contextual coding framework that c…
TVC: Tokenized Video Compression with Ultra-Low Bit Rate
Lebin Zhou, Cihan Ruan, Nam Ling +3
Tokenized visual representations have shown promise in image compression, yet their extension to video remains underexplored due to the challenges posed by complex temporal dynamic…
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Lei Lu, Yize Li, Yanzhi Wang +2
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC…
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content
Lebin Zhou, Kun Han, Nam Ling +2
Image restoration methods like super-resolution and image synthesis have been successfully used in commercial cloud gaming products like NVIDIA's DLSS. However, restoration over ga…
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
Lei Lu, Yanyue Xie, Wei Jiang +3
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous featur…
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Wei Jiang, Wei Wang
We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs i…
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Zhengang Li, Sheng Lin, Shan Liu +4
Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem. We propose FAIVConf, a specially designed video compression fr…
Online Meta Adaptation for Variable-Rate Learned Image Compression
Wei Jiang, Wei Wang, Songnan Li +1
This work addresses two major issues of end-to-end learned image compression (LIC) based on deep neural networks: variable-rate learning where separate networks are required to gen…
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Sheng Lin, Wei Jiang, Wei Wang +4
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.…
Substitutional Neural Image Compression
Xiao Wang, Wei Jiang, Wei Wang +3
We describe Substitutional Neural Image Compression (SNIC), a general approach for enhancing any neural image compression model, that requires no data or additional tuning of the t…