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- Fei Qing WuSuggested from co-authorship
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Works51 from public data
- DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization72
It is argued that the computational cost brought by fine-tuning can be rather unnecessary, and a novel perspective to improving DMG without increasing computational cost is presented, i.e., device-specific parameter generation which directly maps data distribution to parameters.
- Intelligent Model Update Strategy for Sequential Recommendation55
IntellectReq is a novel learning task, aimed at the detection of out-of-distribution data, thereby fine-tuning adaptive communication strategies and evidencing a marked improvement in the efficiency and generalizability of edge-cloud collaborative and dynamic recommendation systems.
- Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives51
An overview of the heterogeneities in data, model, and objective (DMO) is provided and a novel framework called federated mutual learning (FML) is proposed, which enables each client to train a personalized model that accounts for the data heterogeneity (DH).
- Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation35
The Device-Cloud LLM-SRM Collaborative Recommendation Framework (LSC4Rec) is designed under a device-cloud collaboration setting and aims to integrate the advantages of both LLMs and SRMs, as well as the benefits of cloud and edge computing, achieving a complementary synergy.
- Health status prediction for the elderly based on machine learning33
To ascertain visually the performance of these machine learning methods regarding the prediction of the elderly's care needs, the design and verification of the experiment were designed and verified.
- OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning25
OmniWeaving, an omni-level video generation model featuring powerful multimodal composition and reasoning-informed capabilities, is proposed and IntelligentVBench, the first comprehensive benchmark designed to rigorously assess next-level intelligent unified video generation is introduced.
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- HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models22
HyperLLaVA is introduced, which involves adaptive tuning of the projector and LLM parameters, in conjunction with a dynamic visual expert and language expert, respectively, derived from HyperNetworks, enabling dynamic projector and LLM modeling in two-stage training.
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- Semantic Codebook Learning for Dynamic Recommendation Models16
The semantic metacode and semantic codebook, which stores disentangled item representations, ensures robust and accurate parameter generation and demonstrates that SOLID consistently outperforms existing DSR, delivering more accurate, stable, and robust recommendations.
- De-fine: De composing and Re fin ing Visual Programs with Auto-Feedback14
Inspired by benders decomposition, this work introduces De-fine, a training-free framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback, which can improve logical reasoning performance by integrating the strengths of multiple models.
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- Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration8
A universal on-device Multi-modal Model Adaptation framework is introduced, revolutionizing on-device model adaptation by striking a balance between efficiency and effectiveness, and represents a pioneering solution for on-Device Multi-modal Model Adaptation (DMMA).
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- Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs3
This work proposes a unified parameter integration framework that enables modular composition of expert capabilities and introduces a domain compatibility scoring mechanism that quantifies inter-expert alignment at the activation level and correlates with downstream task utility.
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- LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation3
This paper introduces LASER, a tuning-free LLM-driven attention control framework that follows a progressive process: LLM planning, feature-attention injection, and stable animation generation, and proposes a Text-conditioned Image-to-Animation Benchmark to validate the effectiveness and efficacy of LASER.
- Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework3
This paper focuses on the efficient adaptation of EMLLMs to private domains, specifically in two areas: how to reduce data requirements, and how to avoid parameter fine-tuning.
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- Fast Thinking for Large Language Models2
GainRouter is proposed, a lightweight routing mechanism that adaptively switches between fast codebook guided inference and slow explicit reasoning, thereby suppressing overthinking and reducing unnecessary token generation in large language models.
- ART: rule bAsed futuRe-inference deducTion2
This work introduces rule b A sed futu R e-inference deduc T ion ( ART), which aims at de-ducing the correct future event based on the visual phenomenon and the rule-based premises, along with an explanation of the reasoning process.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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