Skip to content

Author

Chuanmin Jia

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Aug 2026

DEC: Low-Bitrate Perceptual Restoration-aware Compression via Decoupled Energy-Complementary Method.

Despite the rapid progress of restoration-aware compression, existing studies still face challenges in low-bitrate restoration scenarios. Real-world image acquisition is inevitably affected by degradations, such as noise, blur, and adverse weather, while subsequent compression further reduces perceptual quality. These coupled degradations make it difficult to recover high-quality images from compact bitstreams. To address this issue, we propose a Decoupled Energy-Complementary method for low-bitrate perceptual restoration-aware compression (DEC). DEC jointly considers acquisition distortions and compression effects within a unified restoration framework. It adopts a decoupled dual-branch architecture with a MainInfo Branch and a SideInfo Branch. The MainInfo Branch preserves structural information for reliable reconstruction, whereas the SideInfo Branch compactly encodes boundary and texture cues with only a few additional bits. These two branches form a compact but informative representation that balances compression efficiency and restoration quality. Extensive experiments show that the SideInfo Branch enhances local restoration details across bitrates, while the MainInfo Branch maintains structural fidelity. Overall, DEC achieves an average 35.49% BD-rate reduction with consistent LPIPS gains over state-of-the-art restoration-aware compression methods, confirming its efficiency and visual quality.

Yuan Xue, Qi Zhang, Shiqi Wang et al. · 0 citations
Preprint Aug 2026

Rethink Before You Execute: Adaptive Execution for World Action Models

World Action Models (WAMs) jointly predict future actions and the evolution of the environment. At each inference, a WAM generates a chunk of actions and the robot executes a fixed prefix before replanning. We argue that this fixed execution horizon is poorly matched to execution dynamics: the chunk reliability varies across task stages, so when to replan depends on the result of accumulated execution, not on the step counts. We propose TempoWAM (Timing Execution by Monitoring Progress Online), a lightweight plug-and-play execution scheme for WAMs. A Recurrent Progress Monitor first estimates task progress from the current observation, task instruction, remaining actions, and execution history; and an Adaptive Execution Protocol then evaluates whether the chunk is advancing the task to decide if replanning is needed. To bridge the training-deployment gap, the protocol is calibrated by a task-dependent calibration factor with online adaptation. Experiments on LIBERO, RoboTwin, and real-world tasks show that TempoWAM consistently improves the efficiency-success trade-off of WAM execution. On real robots, it reduces WAM inferences by 26.9% on easy tasks while maintaining success, and improves success by 13.3 points on difficult tasks.

Feng Ye, Yiming Zhao, Yong Yu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.