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Junhui Liu

5 accepted papers

2026

DuetMerging: Synergizing Dynamic and Static Strategies for Mitigating Task Interference in Model Merging

CVPR 2026

Model merging offers a promising paradigm for consolidating multiple expert models into a single multitask architecture. However, its effectiveness is often hindered by task interference, where conflicting parameter updates from different tasks degrade performance. While dynamic, Mixture-of-Experts

Cited by 0SourceScholar
2026

Position: Embodied AI Requires a Privacy-Utility Tradeoff

ICML 2026poster

Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within \emph{isolated stages} such as instruction, perception, planning and interaction, without consid…

Cited by 0SourceScholar
2023

Preserving Background Sound in Noise-Robust Voice Conversion Via Multi-Task Learning

ICASSP 2023accepted

Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios. However, prior research about VC, mainly focusing on clean voices, pay rare attention to VC with background sound. The critical problem for pre…

Cited by 0SourceScholar
2020

Boundary Content Graph Neural Network for Temporal Action Proposal Generation

ECCV 2020poster

Temporal action proposal generation plays an important role in video action understanding, which requires localizing high-quality action content precisely. However, generating temporal proposals with both precise boundaries and high-quality action content is extremely challenging. To address this is…

Cited by 213SourcePDFScholar