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Laizhong Cui

8 accepted papers

2026

DeNC++: Efficient Diffusion-Enhanced Neural Codec for End-to-end Semantic Streaming at the Edge

AAAI 2026technical

The neural-enhanced video streaming (NeVS) has been an emerging technique to integrate neural models into video codecs for higher streaming efficiency. The state-of-the-art methods, e.g., DeNC and Gemino, typically compress videos in RGB space and restore video quality via a neural enhancement model

Cited by 0SourcePDFScholar
2026

Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning

ICLR 2026poster

Pretrained vision-language models (VLMs), such as CLIP, have shown promise in federated learning (FL) by bringing strong multimodal representations to edge devices. However, continual adaptation remains a core challenge in practical federated settings, where task distributions evolve over time and d…

Cited by 0SourceScholar
2026

ISTER: LINEAR TRANSFORMER FOR EFFICIENT MULTIVARIATE TIME SERIES FORECASTING

ICASSP 2026poster

Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies. However, their widespread adoption is hindered by the quadratic computational complexity of self-attention, which limits scalability on high-dimensional sequ…

Cited by 0SourcePDFScholar
2026

Less Is More in Federated Continual Learning: RieSelect for Conflict-Aware Layer Selection in LLMs

ICML 2026poster

Federated continual learning (FCL) of large language models on edge devices is constrained by a communication--stability--plasticity trilemma. We reveal a less-is-more phenomenon: beyond a moderate layer upload ratio, stability loss offsets saturated plasticity gains, so overall continual performanc…

Cited by 0SourceScholar
2026

Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning

ICLR 2026poster

Vision token pruning has proven to be an effective acceleration technique for the Efficient Vision Language Model (VLM). However, existing pruning methods demonstrate excellent performance preservation in visual question answering (VQA) and suffer substantial degradation on visual grounding (VG) tas…

Cited by 0SourcecodeScholar
2026

Spik4lite: Refactoring Neuromorphic Sparsity for Efficient Spiking Neural Networks on Commodity Edge Devices

ICML 2026poster

Recently, the spiking neural networks (SNNs) have shown great promise in enhancing AI task performance by utilizing the brain-inspired and energy-efficient computational paradigm via the binary (0/1) spikes. Modern SNNs, especially those based on transformers, often require FPGA accelerators or neur…

Cited by 0SourceScholar
2025

Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

AAAI 2025technical

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positi…

2025

DeNC: Unleash Neural Codecs in Video Streaming with Diffusion Enhancement

AAAI 2025technical

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-…